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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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DOE OSTI2017

Open V2X at ESIF: June 2015 - February 2017 (Final Report)

The University of Delaware (UD), and their industrial partners, have carried out testing, documentation, and standards development at ESIF. These activities are intended to help meet the objective of NREL's INTEGRATE program, to enable clean energy technologies to increase the hosting capacity of the grid, by providing grid services in a holistic manner using an open source, interoperable platform. In particular, the 'Open V2X at ESIF' project worked to facilitate standardization and interoperability for Electric Vehicle technologies, including battery electric vehicles and plug-in hybrid vehicles (hence EV) to provide grid services. This was done by interconnecting and testing EV and EVSE (charging station) hardware from different manufacturers, by developing standards and test protocols for standards, and by documenting new protocols to be proposed as standards. Contractor-supplied equipment used in the testing included Vehicle Smart Links (VSLs) installed in an EV and School Bus, single-phase and three-phase charging stations, and an optimizing aggregator (software running on a server, hence just 'Aggregator'). The Aggregator software was developed by U of Delaware and is now licensed to Nuvve Corp for commercial development, and licensed to NRG/EVgo for demonstrations. The Aggregator monitors capacity and operations of the EVs and EVSEs, dispatches them in response to requests, and reports their operations and metered power flow. In operation, as vehicles move, plug-in and unplug, the aggregator tracks the total capacity available within a defined jurisdiction or RTO (Regional Transmission Organization, synonym for ISO or TSO), and dispatches part or all of that capacity based on market or direct dispatch signals from a grid entity. The VSL is the data acquisition and control module in the resource itself (EVSE or EV), with rich communication between a single Optimizing Aggregator and many VSL (one in each resource). All requests and dispatch events are also logged by the aggregator to make the provision of grid power auditable. For these tests, the dispatch was typically a recorded signal, the VSL was in the EV or EVSE, and the recording of experimental performance was often carried out by the normal logging functions of the aggregator.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

New Developments and Capabilities Within WEC-Sim

WEC-Sim is an open-source software for simulating wave energy converters and has been actively developed and applied since its initial release in 2014 to simulate a wide variety of device archetypes. WEC-Sim is developed jointly by the National Renewable Energy Laboratory and Sandia National Laboratories within the MATLAB/SIMULINK environment. A general wave-to-wire model begins with a deployment site resource characterization, which is used to complete the hydrodynamic simulation of wave energy converters (WEC), with the power generation profile imported to a grid simulator to understand the influence on the local electrical network. While modeling the entire wave-to-wire is difficult and encompasses multiple time scales and physics, WEC-Sim is focused on the hydrodynamics simulation to predict, analyze, and optimize WEC dynamics and power performance. WEC-Sim simulations are performed in the time domain based on the radiation and diffraction method using hydrodynamics coefficients derived from boundary element method (BEM)-based frequency-domain potential flow solvers (e.g., WAMIT, NEMOH, Capytaine, or ANSYS-AQWA). With this level of modeling fidelity, WEC-Sim can handle floating body hydrodynamics, mechanical and electrical power generation methods, advanced control implementation, mooring systems, and other unique applications such as desalination. Additional WEC-Sim functionalities include pre-built Simulink blocks and MATLAB scripts that can simulate a wide range of floating systems and the corresponding auxiliary subsystems. The developers of WEC-Sim continue to release new versions of the software, at least annually, with the latest release in September 2022. These releases include bug fixes, updates to software documentation, as well as new features to expand WEC-Sim's capabilities to model a wide range of WEC concepts. This publication will highlight the new features added to WEC-Sim between versions 4.1.0 to 5.0.1, which spans a 2-year period from June 2020 to September 2022. New features described here include topics such as continuous integration checks, revised Morison Element and nonlinear hydro implementations, run directly from Simulink (required for hardware-in-the-loop execution), BEMIO updates to import Capytaine BEM hydrodynamics, addition of cable blocks, and new wave visualization features.

TIDAL AND WAVE POWER↗
DOE OSTI2022

MELCOR Accident Progression and Source Term Demonstration Calculations for a FHR

MELCOR is an integrated thermal hydraulics, accident progression, and source term code for reactor safety analysis that has been developed at Sandia National Laboratories for the United States Nuclear Regulatory Commission (NRC) since the early 1980s. Though MELCOR originated as a light water reactor (LWR) code, development and modernization efforts have expanded its application scope to include non-LWR reactor concepts. Current MELCOR development efforts include providing the NRC with the analytical capabilities to support regulatory readiness for licensing non-LWR technologies under Strategy 2 of the NRC's near- term Implementation Action Plans. Beginning with the Next Generation Nuclear Project (NGNP), MELCOR has undergone a range of enhancements to provide analytical capabilities for modeling the spectrum of advanced non-LWR concepts. This report describes the generic plant model developed to demonstrate MELCOR capabilities to perform fluoride-salt-cooled high-temperature reactor (FHR) safety evaluations. The generic plant model is based on publicly-available FHR design information. For plant aspects (e.g., reactor building leak rate and details of the cover-gas system) that are not described in the FHR references, the analysts made assumptions needed to construct a MELCOR full-plant model. The FHR model uses a TRi-structural ISOtropic (TRISO)-particle fuel pebble-bed reactor with a primary system rejecting heat to two coiled tube air heat ex changers. Three passive direct reactor auxiliary cooling systems provide heat removal to supplement or replace the emergency secondary system heat removal during accident conditions. Surrounding the reactor vessel is a low volume reactor cavity that insulates the reactor with fire bricks and thick concrete walls. A refractory reactor liner system provides water cooling to reduce the concrete wall temperatures. Example calculations are performed to show the plant response and MELCOR capabilities to characterize a range of accident conditions. The accidents selected for evaluation consider a range of degraded and failed modes of operation for key safety functions providing reactivity control, the primary system decay heat removal and also a piping leak of the line to the coolant drain tank.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

New Developments and Capabilities Within WEC-Sim: Preprint

WEC-Sim is an open-source software for simulating wave energy converters, which has been actively developed and applied since its initial release in 2014 to simulate a wide variety of device archetypes. WEC-Sim is developed jointly by the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories (Sandia) within the MATLAB/SIMULINK environment. A general wave-to-wire model begins with a deployment site resource characterization, which is used to complete the hydrodynamic simulation of wave energy converters (WEC), with the power generation profile imported to a grid simulator to understand the influence on the local electrical network. While modeling the entire wave-to-wire is difficult and encompasses multiple time scales and physics, WEC-Sim is focused on the hydrodynamics simulation to predict, analyze, and optimize WEC dynamics and power performance. WEC-Sim simulations are performed in the time domain based on the radiation and diffraction method using hydrodynamics coefficients derived from boundary element method (BEM)-based frequency-domain potential flow solvers (e.g., WAMIT, NEMOH, Capytaine, or ANSYS-AQWA). With this level of modeling fidelity, WEC-Sim can handle floating body hydrodynamics, mechanical and electrical power generation methods, advanced control implementation, mooring systems, and other unique applications such as desalination. Additional WEC-Sim functionalities include pre-built Simulink blocks and MATLAB scripts that can simulate a wide range of floating systems and the corresponding auxiliary subsystems. The developers of WEC-Sim continue to release new versions of the software, at least annually, with our latest release in September 2022. These releases include bug fixes, updates to software documentation, as well as new features to expand WEC-Sim's capabilities to model a wide range of WEC concepts. This publication will highlight the new features added to WEC-Sim between versions 4.1.0 to 5.0.1 which spans over a two year period from June 2020 to September 2022. New features to be described will include topics such as continuous integration checks, revised Morison Element and nonlinear hydro implementations, run directly from Simulink (required for hardware-in-the-loop execution), BEMIO updates to import Capytaine BEM hydrodynamics, addition of cable blocks, and new wave visualization features.

TIDAL AND WAVE POWER↗
DOE OSTI2025

In Situ Surface Reconstruction via Lithium Residue Regulation for Direct Recycling of Ni-Rich Cathodes

Surface stability is crucial for the long cycling performance of Ni-rich cathodes, as it dictates and governs side reactions, preserves crystal integrity, and mitigates capacity degradation during cycling. For spent Ni-rich cathodes targeted for one-step direct recycling, constructing a robust and stable surface is particularly challenging because prior cycling induces severe structural and morphological degradation. Here, in this study, we introduce an in situ surface reconstruction strategy that converts surface lithium residues into a protective layer via a direct liquid-phase coating with ammonium dihydrogen phosphate (ADP). During the process, residual lithium remaining after the hydrothermal relithiation is removed, while a conformal lithium phosphate (Li 3 PO 4 , LPO) layer is formed to act as both a chemical barrier and a structural stabilizer. This dual function enhances cycling stability and rate capability in regenerated cathodes, and the approach is applicable to various Ni-rich compositions including spent LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) and scrap LiNi 0.866 Mn 0.066 Co 0.05 Al 0.018 O 2 (NCMA). Its scalability and compositional versatility make it a promising route for sustainable regeneration of high-performance cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Correlations between Transition Metal Chemistry, Local Structure and Global Structure in Li 2 Ru 0.5 Mn 0.5 O 3 Investigated in a Wide Voltage Window

Li 2 Ru 0.5 Mn 0.5 O 3 , a high capacity lithium rich layered cathode material for lithium-ion batteries, was subject to comprehen-sive diagnostic studies including in situ/ex situ X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), pair distribu-tion function (PDF) and high resolution scanning transmission electron microscopy (STEM) analysis, to understand the cor-relations between transition metal chemistry, structure and lithium storage electrochemical behavior. Ru-Ru dimers have been identified in the as-prepared sample and found to be preserved upon prolonged cycling. Presence of these dimers, which are likely caused by the delocalized nature of 4d electrons, is found to favor the stabilization of the structure in a lay-ered phase. The in situ XAS results confirm the participation of oxygen redox into the charge compensation at high charge voltage, and the great flexibility of the covalent bond between Ru and O may provide great reversibility of the global struc-ture despite of the significant local distortion around Ru. In contrast, the local distortion around Mn occurs at low discharge voltage and is accompanied by a “layered to 1T” phase transformation, which is found to be detrimental to the cycle per-formances. It is clear that the changes of local structure around individual transition metal cations respond separately and differently to lithium intercalation/deintercalation. Here, cations with the capability to tolerate the lattice distortion will benefit for maintaining the integrality of the crystal structure and therefore is able to enhance the long-term cycling performance of the electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Energy Analysis of Combi Heat Pump System Configurations for Space Conditioning and Domestic Hot Water Heating in Residential Buildings

Combi heat pump systems, also referred to multifunctional variable refrigerant flow heat recovery (MF-VRFHR) systems, are specifically designed for residential applications to manage both space conditioning and domestic hot water (DHW). They have attracted attention due to their potential for energy conservation through heat recovery. The incorporation of a hot water tank introduces various system configurations, each characterized by distinct pros and cons related to energy efficiency, system stability, and maintenance. Despite this, a critical gap exists as the specific energy performance remains unquantified under diverse operational modes (e.g., heating mode and heat recovery mode). This paper aims to bridge this gap by conducting a comprehensive comparative analysis of two prevalent system configurations while considering feasible proposed control logics. Configuration 1 integrates a separate hot water tank and a refrigerant-to-water heat exchanger (HEX), also known as a Hydro Kit while Configuration 2 incorporates a refrigerant-wrapped hot water tank. To facilitate this analysis, we developed high-fidelity system models for both configurations in Modelica, capturing system dynamics and detailed control sequences effectively. These system models were built upon the TIL library for HVAC equipment components and the Buildings library for residential building thermal load calculations. The validation of the simulation testbed utilized data from experiments conducted in the PNNL lab home for Configuration 1. To establish the simulation testbed for Configuration 2, we extended the modeling setup derived from Configuration 1. This extension specifically involved substituting the separate hot water tank and Hydro Kit with a refrigerant-wrapped hot water tank of similar sizing sourced from an actual product. The simulation analysis of heating-only and heat recovery modes reveals that Configuration 2 not only saves energy and maintains warmer tank temperatures but also demonstrates faster water heating capabilities. This is attributed to decreased energy loss and improved heat transfer. The study encompasses a wide range of scenarios, considering diverse thermal loads and water usage patterns across heating and heat recovery modes. Overall, the comprehensive results indicate that Configuration 2 achieves energy savings ranging from 3.5% to 12.2% compared to Configuration 1, depending on factors such as water usage patterns, thermal loads, and operational modes.

Configuration, Comparison, Multi-functional, Resid↗
DOE OSTI2018

Thermal scattering law of C 2 H 4 n : Integrating experimental data with DFT calculations

Improvements in determination of the thermal scattering law of moderator materials (measuring, calculating and validating) are important for accurate prediction of neutron thermalization in nuclear systems. In this paper a methodology for producing thermal scattering libraries from the experimental data for polyethylene C 2 H 4 n is discussed. Double differential scattering cross section (DDSCS) experiments were performed at the Spallation Neutron Source of Oak Ridge National Laboratory (SNS ORNL). New scattering kernel evaluations, based on phonon spectrum for C 2 H 4 n , are created using the NJOY2016 code. Two different methods were used: direct and indirect geometry neutron scattering at ARCS and SEQUOIA, and VISION instruments, respectively, where the phonon spectrum was derived from the dynamical structure factor S(Q, ω ) obtained from the measured DDSCS. In order to compare and validate the newly created library, the experimental setup was simulated using MCNP6.1. Compared with the current ENDF/B-VII.1, the resulting RPI C 2 H 4 n libraries improved both double differential scattering and total scattering cross sections. A set of criticality benchmarks containing C 2 H 4 n from HEU-MET-THERM resulted in an overall improved calculation of K eff , although the libraries should be tested against benchmarks more sensitive to C 2 H 4 n . The DFT + oClimax method is used and is shown to be most comprehensive method for analysis of moderator materials. Finally, the importance of DFT + oClimax method lies in the fact that it can be validated against all data measured at VISION, ARCS and SEQUOIA, and experimental total scattering cross section measurements.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

A second‐generation virtual‐pinhole PET device for enhancing contrast recovery and improving lesion detectability of a whole‐body PET/CT scanner

Purpose We have developed a second‐generation virtual‐pinhole (VP) positron emission tomography (PET) device that can position a flat‐panel PET detector around a patient's body using a robotic arm to enhance the contrast recovery coefficient (CRC) and detectability of lesions in any region‐of‐interest using a whole‐body PET/computed tomography (CT) scanner. Methods We constructed a flat‐panel VP‐PET device using 32 high‐resolution detectors, each containing a 4 4 MPPC array and 16 16 LYSO crystals of 1.0 1.0 3.0 mm 3 each. The flat‐panel detectors can be positioned around a patient's body anywhere in the imaging field‐of‐view (FOV) of a Siemens Biograph 40 PET/CT scanner by a robotic arm. New hardware, firmware and software have been developed to support the additional detector signals without compromising a scanner's native functions. We stepped a 22 Na point source across the axial FOV of the scanner to measure the sensitivity profile of the VP‐PET device. We also recorded the coincidence events measured by the scanner detectors and by the VP‐PET detectors when imaging phantoms of different sizes. To assess the improvement in the CRC of small lesions, we imaged an elliptical torso phantom measuring 316 228 162 mm 3 that contains spherical tumors with diameters ranging from 3.3 to 11.4 mm with and without the VP‐PET device. Images were reconstructed using a list mode Maximum‐Likelihood Estimation‐Maximization algorithm implemented on multiple graphics processing units (GPUs) to support the unconventional geometries enabled by a VP‐PET system. The mean and standard deviation of the CRC were calculated for tumors of different sizes. Monte Carlo simulation was also conducted to image clusters of lesions in a torso phantom using a PET/CT scanner alone or the same scanner equipped with VP‐PET devices. Receiver operating characteristic (ROC) curves were analyzed for three system configurations to evaluate the improvement in lesion detectability by the VP‐PET device over the native PET/CT scanner. Results The repeatability in positioning the flat‐panel detectors using a robotic arm is better than 0.15 mm in all three directions. Experimental results show that the average CRC of 3.3, 4.3, and 6.0 mm diameter tumors was 0.82%, 2.90%, and 5.25%, respectively, when measured by the native scanner. The corresponding CRC was 2.73%, 6.21% and 10.13% when imaged by the VP‐PET insert device with the flat‐panel detector under the torso phantom. These values may be further improved to 4.31%, 9.65% and 18.01% by a future dual‐panel VP‐PET insert device if DOI detectors are employed to triple its detector efficiency. Monte Carlo simulation results show that the tumor detectability can be improved by a VP‐PET device that has a single flat‐panel detector. The improvement is greater if the VP‐PET device employs a dual‐panel design. Conclusions We have developed a prototype flat‐panel VP‐PET device and integrated it with a clinical PET/CT scanner. It significantly enhances the contrast of lesions, especially for those that are borderline detectable by the native scanner, within regions‐of‐interest specified by users. Simulation demonstrated the enhancement in lesion detectability with the VP‐PET device. This technology may become a cost‐effective solution for organ‐specific imaging tasks.

Jiang, Jianyong↗
DOE OSTI2026

A solid-state extrusion pathway extensible to upcycling aluminum scrap and end-of-life permanent magnets into ductile Al-6061/SmCo 5 structural magnetic composites

Permanent magnets offer strong potential for multifunctional structural components, but their inherent brittleness limits direct integration. Here, SolidStir® Extrusion (SSE) is used to embed SmCo5 powder into AA6061 in the solid state, and the resulting microstructure, interfacial chemistry, mechanical properties, and magnetic response are systematically characterized. SSE fragments the initial SmCo 5 powder into predominantly fine particles dispersed within an ultrafine Al matrix, with a volume fraction of ∼ 4–6 % in this proof-of-concept study. Interfacial reactions form a continuous Al–Co reaction layer, with local atomic-resolution indexing consistent with Al 13 Co 4 , together with Sm–Si-rich reaction products, while the matrix contains Sm–Co–Si-rich precipitates and Mg 2 Si, producing features from nanometers to micrometers. Compared with an Al extrudate, this architecture increases yield strength by ∼ 42 % and tensile strength by ∼ 31 %, while preserving uniform elongation, and it sustains work hardening in compression from 150 to 400 °C. The composite retained hard-magnetic behavior, with axial magnetization of 12.7 A·m 2 kg −1 at 6 T and intrinsic coercivity of 9.6 kOe. These findings demonstrate that SSE can produce Al-based load-bearing magnetic composites while preserving useful SmCo5 functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Analysis and Planning Framework for Nuclear Plant Transformation

Commercial nuclear power in the United States has been an unqualified success by any measure, providing low cost, carbon-free, and safe baseload electricity for decades. The industry today is at the peak of its historical performance in terms of generation output, reliable operations, and demonstrated nuclear safety. However, it is no longer among the lowest-cost electric generation sources with the emergence of subsidized renewables and shale gas generation. The business model that has served the operating nuclear fleet so well over its life is now a drag on cost performance due to its reliance on a large highly skilled labor force. In contrast, digital technology and innovation are enabling dramatic efficiencies in energy production, resulting in fierce competition for a commodity such as electricity. The nuclear power industry has responded to this challenge with many initiatives to improve efficiency and modernize plant equipment, especially where reliability and obsolescence issues are pressing. However, it would be a missed opportunity to merely modernize the plant components and the work processes of an outdated business model that was formulated to manage the technology of the 1960s. Rather, the greater opportunity is to transform that business model to one that fully exploits the capabilities of modern digital technology, resulting in substantially lower production costs and sustainable market viability. One successful example of such transformation is the concept of Integrated Operations, which was introduced into the North Sea oil and gas industry a couple of decades ago when the profitability of operating these fields was severely threatened by low world petroleum prices and the high overhead of operating the offshore oil and gas platforms. This effort resulted in significant changes to how these oil fields were operated and allowed the industry to continue profitable operation of these platforms. This example has some remarkable parallels to the U.S. commercial nuclear industry as described in the next section. This report provides an analysis and planning framework for such a nuclear plant operating model transformation based on the transferable learnings from the North Sea transformation. This framework is termed Integrated Operations for Nuclear or ION. This report describes the key principles and methods of Integrated Operations and how they are being applied in a collaboration between the DOE Light Water Reactor Sustainability Program and Xcel Energy in an initiative to transform its operating model for performance improvement and long-term sustainability. It describes a method to bring the operating costs of a nuclear fleet in line with market-based pricing, transforming work functions to reduce cost with technology innovations. This initiative will continue over the next several years in the detailed development of transformative concepts for nuclear plants, which will be published as follow-up to this initial report on Integrated Operations for Nuclear.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2022

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗
DOE OSTI2026

Thermal cycling-driven microstructural changes of eutectic Al–Si phase change materials in SS304 containers revealed by multi-modal imaging

Aluminum-based Al–Si alloys are widely used as phase change materials (PCMs) in thermal energy storage (TES) systems owing to their high volumetric latent heat and superior thermal conductivity. However, their long-term reliability is limited by degradation processes that remain insufficiently understood. In this work, we employ a multimodal, correlative characterization framework to systematically resolve the degradation behavior of eutectic Al–Si PCMs in contact with SS304 containers under repeated thermal cycling. By integrating high-resolution electron microscopy, three-dimensional X-ray fluorescence (3D XRF) imaging, and differential scanning calorimetry (DSC), we directly link spatially resolved compositional and microstructural evolution to changes in thermophysical properties. The correlative analysis reveals that elemental leaching of Fe, Cr, and Ni from the stainless-steel container into the PCM drives the formation of intermetallic compounds (IMCs) both at the interface and within the bulk PCM, leading to pronounced compositional heterogeneity. These interfacial reactions and diffusion-induced transformations progressively destabilize the Al–Si eutectic, reducing the effective phase-transforming fraction. Consistent with these observations, DSC measurements show a decrease in melting temperature and latent heat of fusion with thermal cycling. These results underscore the critical influence of interfacial reactions and material compatibility on the stability, durability, and overall performance of Al–Si-based TES systems.

25 ENERGY STORAGE↗
DOE OSTI2018

Sandia Cloud Forensics and Incident Response Platform v 1.0

As systems and devices become virtualized and deployed in the cloud, the hypervisor becomes an increasingly appropriate place to collect performance data, system state, system landscape, function calls, transaction traces, and other characteristics. We developed a method by which an introspection application may be coupled with a hypervisor to “reach into” the VM with minimal intrusiveness to collect data critical to the reconstruction of events, files, and operations. Such a capability is required to take advantage of the hypervisor as an instrumentation platform and to integrate that data with more traditional collection mechanisms. The concept of a VM serviced by a lightweight hypervisor is a relatively new paradigm for forensic practitioners. Traditional forensic techniques, based on assumptions that the filesystem was directly interacting with the hardware through an abstraction, afforded the forensic practitioner the assumption that there was nothing controlling the application below the filesystem. This is not the case when using virtualized technologies. Hypervisors can covertly monitor, introspect and interact with the guest in a transparent fashion. The problems of storage and collection of actionable data are exhausting. The current challenge is most hypervisors do not expose a useful application programming interface (API) at a sufficient level to do transparent, fine-grained and customizable introspection. Scalable VM instrumentation and introspection at an in-depth level requires fast handling of events, as well as direct access to VM state. Furthermore, deep introspection benefits greatly from the ability to gather data from the hardware during the VM’s exit to the hypervisor. All of this requires identical access to the system as the hypervisor itself; improper use of this ability could easily cause system instability. It is for this reason we believe that the hypervisor developers have been hesitant to grant this much control through their APIs. However, our approach leverages other means to collect and monitor the guests in a targeted fashion. Virtual machine introspection (VMI) is a technique used to monitor the runtime state of a system-level virtual machine. The runtime state can be may include processor registers, memory, disk, network, and any other hardware-level events A review of research literature and current VMI technologies exposed several limitations and tradeoffs in VMI approaches, including: the use of in-guest agents; kernel to user space transitions (dramatically slowing down processing); VMI tool pre-configuration requirements; hypervisor version lock-in or source code patching; reliance on operating system symbols; limited processor features due to hypervisor (even if the hardware could do more). To address these constraints, a VMI tool was envisioned to provide the cloud forensic capabilities while having as few of these limitations as possible. The Kernel-based Virtual Machine Introspection (KVMi) tool was developed for hypervisors on Intel’s x86-64 architecture. To meet performance, scoping and use-case demands, the follow criteria were applied to KVMi to support live, forensic data collection: • Shall not require in-guest agents. • Shall work with any recent version of modern hypervisors. • Shall introspect upon any guest (VM) running modern versions of Windows, Linux and OS X. • Shall have the ability to find and track operating system artifacts • Shall not require operating system symbol files. • Shall be able to fully handle VM-exits, bypassing execution of the hypervisor if necessary, to facilitate new features the hypervisor may not support. • Shall be compilable/loadable on a running system with standard build tools.

Stout, WilliamM.S.↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermal processing to modulate surface chemistry and bulk charge distribution in nickel-rich layered lithium positive electrodes

The broader application of nickel-rich layered oxides as positive electrode materials for lithium-ion batteries has been hindered by their high manufacturing cost and inferior cycling stability. Thermal processing, which is integral to electrode materials manufacturing and fundamental in materials science, has not been fully utilized to design advanced positive electrode materials. Herein, we demonstrate the capability of using quenching heat treatment to regulate Li distribution and modulate electronic structure near particle surface. The resulting materials exhibit less parasitic reactions with the electrolyte and an improved charge distribution homogeneity in secondary particles, leading to more stable cycling performance at high voltages (4.5 V vs Li/Li + ). Our synchrotron X-ray analyses reveal the underlying interplay between surface structure and bulk charge distribution in positive electrode materials particles. While strategies used to stabilize positive electrode materials through compositional control, surface modification, and electrolyte engineering have become mature, thermal processing can be advantageous to further improve positive electrode materials manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Green Electrode Processing Enabled by Fluoro-Free Multifunctional Binders for Lithium-Ion Batteries

The eco-friendly processing of conjugated polymer binder for lithium-ion batteries demands improved polymer solubility by introducing functional moieties, while this strategy will concurrently sacrifice polymer conductivity. Employing the polyfluorene-based binder poly(2,7-9,9 (di(oxy-2,5,8-trioxadecane))fluorene) (PFO), soluble in water-ethanol mixtures, a novel approach is presented to solve this trade-off, which features integration of aqueous solution processing with subsequent controlled thermal-induced cleavage of solubilizing side chains, to produce hierarchically ordered structures (HOS). The thermal processing can enhance the intermolecular π–π stacking of polyfluorene backbone for better electrochemical performance. Notably, HOS-PFO demonstrated a substantial 6–7 orders of magnitude enhancement in electronic conductivity, showcasing its potential as a functional binder for lithium-ion batteries. As an illustration, HOS-PFO protected SiOx anodes, utilizing in situ side chain decomposition of PFO surrounding SiOx particles after aqueous processing are fabricated. HOS-PFO contributed to the stable cycling and high-capacity retention of practical SiOx anodes (3.0 mAh cm -2 ), without the use of any conducting carbon additives or fluorinated electrolyte additives. It is proposed that this technique represents a universal approach for fabricating electrodes with conjugated polymer binders from aqueous solutions without compromising conductivity.

25 ENERGY STORAGE↗