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At least 559 records · Page 31

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DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

09 BIOMASS FUELS↗

DOE OSTI2024

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (T g ) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict T g values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 T g values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 T g values and produced an RMSE of 34.5 °C. In conclusion, this work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Managing complexity in simulations of land surface and near-surface processes

Increasing computing power and the growing role of simulation in Earth systems science have led to an increase in the number and complexity of processes in modern simulators. We present a multiphysics framework that specifies interfaces for coupled processes and automates weak and strong coupling strategies to manage this complexity. Process management is enabled by viewing the system of equations as a tree, where individual equations are associated with leaf nodes and coupling strategies with internal nodes. A dynamically generated dependency graph connects a variable to its dependencies, streamlining and automating model evaluation, easing model development, and ensuring models are modular and flexible. Additionally, the dependency graph is used to ensure that data requirements are consistent between all processes in a given simulation. Here we discuss the design and implementation of these concepts within the Arcos framework, and demonstrate their use for verification testing and hypothesis evaluation in numerical experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Enantiospecific heterogeneous catalysis utilizes chiral surfaces to resolve enantiomers via structure sensitive surface chemistry. The catalyst design challenge is the identification of chiral surface structures that maximize enantiospecificity. Herein, we develop data driven models for the enantiospecificity of tartaric acid reactions on chiral Cu(hkl) R&S surfaces. Measurements of enantiospecific rate constants were obtained by using curved Cu(hkl) R&S surfaces that enable kinetic measurements on hundreds of chiral surface orientations. One model uses feature vectors derived from generalized coordination numbers to capture the local structure around Cu atoms exposed by the Cu(hkl) R&S surfaces. The second model introduces the use of chiral cubic harmonic functions to capture the symmetry constraints of the face-centered cubic Cu structure. The model using 58 generalized coordination numbers has a fitting error similar to that of the model using only 5 cubic harmonic functions. The two models predict maxima in the enantiospecificity on surfaces with very similar surface orientations. The models developed in this work are applicable for any enantiospecific reaction happening on any chiral material with a cubic lattice structure, opening the way to understanding the surface structure sensitivity of the enantiospecific reaction kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Diffusioosmotic flow reversals due to ion–ion electrostatic correlations

Existing theories of diffusioosmosis have neglected ion–ion electrostatic correlations, which are important in concentrated electrolytes. Here, we develop a mathematical model to numerically compute the diffusioosmotic mobilities of binary symmetric electrolytes across low to high concentrations in a charged parallel-plate channel. We use the modified Poisson equation to model the ion–ion electrostatic correlations and the Bikerman model to account for the finite size of ions. We report two key findings. First, ion–ion electrostatic correlations can cause a unique reversal in the direction of diffusioosmosis. Such a reversal is not captured by existing theories, occurs at ≈ 0.4 M for a monovalent electrolyte, and at a much lower concentration of ≈ 0.003 M for a divalent electrolyte in a channel with the same surface charge. This highlights that diffusioosmosis of a concentrated electrolyte can be qualitatively different from that of a dilute electrolyte, not just in its magnitude but also its direction. Second, we predict a separate diffusioosmotic flow reversal, which is not due to electrostatic correlations but the competition between the underlying chemiosmosis and electroosmosis. This reversal can be achieved by varying the magnitude of the channel surface charge without changing its sign. However, electrostatic correlations can radically change how this flow reversal depends on the channel surface charge and ion diffusivity between a concentrated and a dilute electrolyte. Furthermore, the mathematical model developed here can be used to design diffusioosmosis of dilute and concentrated electrolytes, which is central to applications such as species mixing and separation, enhanced oil recovery, and reverse electrodialysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Thermoeconomic cost optimization of superconducting magnets for proton therapy gantries

A compact gantry delivering 70-220 MeV protons with fixed field in the superconducting magnets could reduce the cost and improve the adoption of proton therapy. While a number of magnet and cryogenics designs have been proposed, the combined capital and operating costs of state-of-the-art superconducting materials have not been analyzed. In response, we develop a thermoeconomic model of a multi-stage, conduction cooled gantry lattice and analyze the cryocooler operating cost, cryocooler capital cost and conductor capital cost for Nb-Ti, Nb 3 Sn, REBCO and Bi-2223 over a continuous range of magnet temperatures, and a differential evolution algorithm is used to identify the optimal combination of thermal intercept temperatures. Although Nb3Sn yields the lowest Net Present Value (NPV) of $111.7k at a magnet temperature of 9.4 K, the optimized Bi-2223 design at 12.8 K approaches the realm of commercial feasibility by offering improved thermal stability and forgoing the need for costly conductor heat treatment and magnet quench training. Furthermore, it was found that Nb3Sn was more cost effective than Nb-Ti and that REBCO was not economically viable for the parameters of this investigation. Overall, the thermoeconomic model developed herein can optimize conductor choices, magnet temperatures and thermal staging which has value for any conduction-cooled superconducting magnet.

BSCCO↗

DOE OSTI2021

Flow Assisted Evaporative Cooling for Electric Motor

This paper examines a novel concept of flow assisted latent heat driven two-phase evaporative cooling (EC) confined in-between slot liner and active-winding of electric motor. Wicking micro-structure enhanced PDMS liner axially sucks coolant in the form of thin film between the PDMS liner and active-winding and eventually enables thin film evaporation on the outer surface of the active-winding. Therefore, EC based thermal management eliminates contact resistance between the winding and slot-liner and, enhances the heat extraction from the winding without compromising the electro-magnetic performance. Two-way coupled electro-magnetic (EM) – computational fluid dynamics/heat transfer (CFD/HT) and EM - lumped parameter thermal network (LPTN) models have been developed to assess the electro-thermal performance of the EC under steady and transient conditions. Taking a case study of a 125 kW jacket cooled BMW i3 motor and dielectric coolant FC-84, EC is shown to be capable of handling a maximum steady state rms current density of 26 A/mm2 at a evaporative heat transfer coefficient of 5,000 W/m2.K, which is about 78.7% higher compared to the traditional jacket cooling (JC). In case of EC, a maximum steady state and transient rms current density of 30 A/mm2 (106.2% higher compared to the JC), and 40.8 A/mm2 have been realized by using high thermal conductivity epoxy (1.9 W/m.K) impregnation material. Thermal performance of the EC is also assessed and compared with JC over a dynamic drive cycle. Lastly, a motorette testing has been performed to demonstrate the applicability of the proposed EC method and to validate the developed modeling framework.

42 ENGINEERING↗

DOE OSTI2025

Effects cascade debris and helium bubbles on the strength of aged plutonium

The radioactive decay of aging Pu is dominated by α-decay. This persistent α-decay produces crystalline defects in the form of dislocation loops and helium bubbles that evolve with time. Comparable defects are produced in other metallic alloys when subject to neutron irradiation, and these defects are known to modify the plastic deformation of irradiated materials. Models have been developed for these irradiated materials and validated against experimental confirmations of yield strength and the concomitant microstructural evolution. In this paper, we deploy those previously developed models and apply their mechanics to plutonium aging.

Chemical elements↗

DOE OSTI2017

Protein structure determination using metagenome sequence data

Filling in the protein fold picture Fewer than a third of the 14,849 known protein families have at least one member with an experimentally determined structure. This leaves more than 5000 protein families with no structural information. Protein modeling using residue-residue contacts inferred from evolutionary data has been successful in modeling unknown structures, but it requires large numbers of aligned sequences. Ovchinnikov et al. augmented such sequence alignments with metagenome sequence data (see the Perspective by Söding). They determined the number of sequences required to allow modeling, developed criteria for model quality, and, where possible, improved modeling by matching predicted contacts to known structures. Their method predicted quality structural models for 614 protein families, of which about 140 represent newly discovered protein folds. Science , this issue p. 294 ; see also p. 248

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

AI for Earthquake Physics

The core LANL program sponsored by Office of Science, Basic Energy Science, Chemical Sciences, Geosciences, and Biosciences (DOE-BES-CSGB) and led by PI Johnson aims to research earthquake faults to advance fault physics and earthquake hazards. All work completed is required to be made publicly available through publications and open-source codes supporting the published results. All routines are/will-be written in open source python and applied to publicly available data sets. These routines will format data from input into models, develop and test modeling frameworks for the problems addressed, and produce figures applicable to peer-reviewed manuscripts. All work is reviewed for Los Alamos Unlimited Release before submitting to a journal. This summary encompasses recently completed work and work to be complete for the duration of the program.

Johnson, Christopher↗

DOE OSTI2013

Potential Economic Impacts from Offshore Wind in the Southeast Region (Fact Sheet)

Offshore wind is a clean, renewable source of energy and can be an economic driver in the United States. To better understand the employment opportunities and other potential regional economic impacts from offshore wind development, the U.S. Department of Energy (DOE) funded research that focuses on four regions of the country. The studies use multiple scenarios with various local job and domestic manufacturing content assumptions. Each regional study uses the new offshore wind Jobs and Economic Development Impacts (JEDI) model, developed by the National Renewable Energy Laboratory. This fact sheet summarizes the potential economic impacts identified by the study for the Southeast (defined here as Georgia, South Carolina, North Carolina, and Virginia).

17 WIND ENERGY↗

DOE OSTI2014

Potential Economic Impacts from Offshore Wind in the Mid-Atlantic Region (Fact Sheet)

17 WIND ENERGY↗

DOE OSTI2014

Potential Economic Impacts from Offshore Wind in the Gulf of Mexico Region (Fact Sheet)

17 WIND ENERGY↗

DOE OSTI2014

Potential Economic Impacts from Offshore Wind in the Great Lakes Region (Fact Sheet)

Offshore wind is a clean, renewable source of energy and can be an economic driver in the United States. To better understand the employment opportunities and other potential regional economic impacts from offshore wind development, the U.S. Department of Energy (DOE) funded research that focuses on four regions of the country. The studies use multiple scenarios with various local job and domestic manufacturing content assumptions. Each regional study uses the new offshore wind Jobs and Economic Development Impacts (JEDI) model, developed by DOE's National Renewable Energy Laboratory. This fact sheet summarizes the potential economic impacts identified by the study for the Great Lakes region.

17 WIND ENERGY↗

DOE OSTI2015

Aging Behavior of the EPDM O-Rings in the H1616 Shipping Package

The H1616 shipping package is used within the DOE complex for shipping tritium reservoirs. The annual recertification frequency can create logistical difficulties with other constraints on the timing of shipments; thus, a longer re-certification period is desirable. The ethylene propylene diene monomer (EPDM) O-rings used in the H1616 shipping package are being aged and tested at the Savannah River National Laboratory (SRNL) to provide a technical basis for extending the annual maintenance of the H1616 shipping package. H1616 EPDM O-rings are being aged at elevated temperature, and tested for degradation in mechanical properties, compression stress relaxation (CSR) behavior, and leak performance. Mechanical properties of aged O-rings show significant degradation can occur, but an inert atmosphere (argon backfill) greatly reduces the rate of degradation. The CSR behavior of O-rings was evaluated in air at 79 to 177 °C. These collective data were used to develop a predictive model for extrapolation of CSR behavior to relevant service temperatures (<67 °C). O-rings were also aged in H1616 Containment Vessels (CV) in an inert atmosphere at 71 to 149 °C. The vessels are helium leak tested periodically to determine if they continue to remain leak-tight. The vessel tests provide a solid demonstration that the H1616 O-rings will remain leak-tight at temperatures up to 113 °C for up to approximately 2.3 years. Significantly longer periods of leak-tight service are expected at the lower temperatures actually experienced in service. The predictive model developed from the CSR data conservatively indicates a service life of ~5 years at 67 °C. Although the relationship between CSR behavior and leak-tight performance has not been established for this design, the CSR predictions for this O-ring are conservative relative to leak-tight performance. Based on the collective data developed to date, SRNL has recommended that the maintenance interval for the H1616 package be extended from 1 year to 2 years. Future recommendations in support of even longer maintenance intervals are expected, as additional aging data become available.

36 MATERIALS SCIENCE↗

DOE OSTI2011

Macroalgae Analysis: A National GIS-based Analysis of Macroalgae Production Potential (Summary Report and Project Plan)

The overall project objective is to conduct a strategic analysis to assess the state of macroalgae as a feedstock for biofuels production. The objective in FY11 is to develop a multi-year systematic national assessment to evaluate the U.S. potential for macroalgae production using a GIS-based assessment tool and biophysical growth model developed as part of these activities. The initial model development for both resource assessment and constraints was completed and applied to the demonstration areas. The model for macroalgal growth was extended to the EEZ off the East and West Coasts of the United States, and a plan to merge the findings for an initial composite assessment was developed. In parallel, an assessment of land-based, port, and offshore infrastructure needs based on published and grey literature was conducted. Major information gaps and challenges encountered during this analysis were identified. Also conducted was an analysis of the type of local, state, and federal requirements that pertain to permitting land-based facilities and nearshore/offshore culture operations

09 BIOMASS FUELS↗

DOE OSTI2016

Mesoscale to microscale wind farm flow modeling and evaluation

The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface-layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, ‘building the bridge’ means developing models for the so-called ‘terra incognita,’ a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary-layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to direct future research in this area.

17 WIND ENERGY↗