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DOE OSTI2023

Global-Local Policy Search and its Application in Grid-Interactive Building Control

As the buildings sector represents over 70% of the total U.S. electricity consumption, it offers a great amount of untapped demand-side resources to tackle many critical grid-side problems and improve the overall energy system's efficiency. To help make buildings grid-interactive, this paper proposes a global-local policy search method to train a reinforcement learning (RL) based controller which optimizes building operation during both normal hours and demand response (DR) events. Experiments on a simulated five-zone commercial building demonstrate that by adding a local fine-tuning stage to the evolution strategy policy training process, the control costs can be further reduced by 7.55% in unseen testing scenarios. Baseline comparison also indicates that the learned RL controller outperforms a pragmatic linear model predictive controller (MPC), while not requiring intensive online computation.

demand response↗

DOE OSTI2018

Processing and Characterization of Ag-Cu-Sn Brazing Alloy Prepared by a Mechanical Alloying Method

Mechanical alloying was used to prepare an Ag-Cu-Sn brazing alloy with the low amounts of intermetallic compounds. X-ray diffraction and differential scanning calorimetry were carried out to investigate the phase transformation of the as-milled samples. It seemed that the intermetallic compounds experienced the stage of synthesis-decomposition-resynthesis in the ball milling process. By controlling the ball milling time and tin contents, the solid solution phases of Ag (Cu, Sn) and Ag (Cu) were achieved in the 60Ag30Cu10Sn sample after milling for 40 h. Although the intermetallic compound will be generated during the brazing process, the improvement of their distributed morphology is beneficial for the good bonding interface of the joint with stainless steel substrate.

36 MATERIALS SCIENCE↗

DOE OSTI2011

Malondialdehyde inhibits an AMPK-mediated nuclear translocation and repression activity of ALDH2 in transcription

Research highlights: {yields} ALDH2 is an MDA-modified protein in old rat kidney tissues. {yields} AMPK associates with ALDH2 and triggers the nuclear localization of ALDH2. {yields} ALDH2 serves as a general transcriptional repressor by associating with HDACs. {yields} MDA inhibits the AMPK-mediated translocation of ALDH2 and its repression activity. -- Abstract: Aging process results from deleterious damages by reactive oxygen species, in particular, various metabolic aldehydes. Aldehyde dehydrogenase 2 (ALDH2) is one of metabolic enzymes detoxifying various aldehydes under oxidative conditions. AMP-activated protein kinase (AMPK) plays a key role in controlling metabolic process. However, little was known about the relationship of ALDH2 with AMPK under oxidative conditions. Here, we, by using MDA-specific monoclonal antibody, screened the tissues of young and old rats for MDA-modified proteins and identified an ALDH2 as a prominent MDA-modified protein band in the old rat kidney tissue. ALDH2 associates with AMPK and is phosphorylated by AMPK. In addition, AICAR, an activator of AMP-activated protein kinase, induces the nuclear translocation of ALDH2. ALDH2 in nucleus is involved in general transcription repression by association with histone deacetylases. Furthermore, MDA modification inhibited the translocation of ALDH2 and the association with AMPK, and ultimately led to de-repression of transcription in the reporter system analysis. In this study, we have demonstrated that ALDH2 acts as a transcriptional repressor in response to AMPK activation, and MDA modifies ALDH2 and inhibits repressive activity of ALDH2 in general transcription. We thus suggest that increasing amount of MDA during aging process may interrupt the nuclear function of ALDH2, modulated by AMPK.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation, we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive split transcription factors in the budding yeast, Saccharomyces cerevisiae. We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Nucleation and growth of Nb nanoclusters during plasma gas condensation

Niobium nanoclusters were produced using a plasma gas condensation process. The influence of gas flow rate, aggregation length, and source current on the nanocluster nucleation and growth were analyzed. Nanoclusters with an average diameter from 4 nm to 10 nm were produced. Cluster size and concentration were tuned by controlling the process inputs. The effects of each parameter on the nucleation zone, growth length, and residence time was examined. The parameters do not affect the cluster formation and growth independently; their influence on cluster formation can be either cumulative or competing. Examining the nucleation and growth over a wide combination of parameters provided insight into their interactions and the impact on the growth process. These results provide the opportunity for a broader understanding into the nucleation and growth of nanoclusters and some insights into how process parameters interact during deposition. This knowledge will enhance the ability to create nanoclusters with desired size dispersions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Use of Stable Mercury Isotopes to Assess Mercury and Methylmercury Transformation and Transport across Critical Interfaces from the Molecular to the Watershed Scale (Final Report)

This project titled “Use of Stable Mercury Isotopes to Assess Mercury and Methylmercury Transformation and Transport across Critical Interfaces from the Molecular to the Watershed Scale” represents a collaborative effort between the University of Michigan (Jason Demers, PI) and Oak Ridge National Laboratory (Scott Brooks, co-I). Much has been learned about mercury (Hg) cycling in stream ecosystems, and East Fork Poplar Creek (EFPC) in particular, through decades of previous research. Nevertheless, some of the most fundamental questions regarding the sources of bioavailable Hg and its transformation to toxic methylmercury (MeHg) have remained unanswered. These fundamental questions include: (1) what are the sources and biogeochemical processes that lead to the input of dissolved Hg to stream water across critical subsurface interfaces within stream ecosystems, and EFPC in particular? and (2) what are the sources and biogeochemical processes that control the production and fate of bioaccumulative MeHg within stream ecosystems, and in EFPC in particular? To address these fundamental questions, our project aimed to couple laboratory experiments and field observations, both utilizing natural abundance Hg stable isotope techniques, to identify the processes responsible for generating mobile, bioavailable dissolved Hg from recalcitrant legacy sources within critical subsurface zones (e.g., streambed hyporheic zone, riparian floodplain subsurface). We used the isotopic signature of this bioavailable dissolved Hg to track its mobilization across these critical interfaces in order to link diffuse subsurface sources of dissolved Hg with increases in surface water dissolved Hg flux measured at the watershed scale. Additionally, our research aimed to determine the isotopic composition of MeHg within these same critical subsurface zones. We directly assessed the isotopic composition of MeHg within biota in order to gain insight into which subsurface sources of inorganic Hg and toxic MeHg are available for bioaccumulation within the EFPC ecosystem. Net fluxes of dissolved Hg along the flow path of EFPC were shown to vary spatially and temporally. In the Upper EFPC, within the Y12 boundary, stream water flux of dissolved Hg consistently decreased between the outfall and the downstream boundary of Y12 (57% ± 29%, 1SD). Within the Upper EFPC, an assessment of Hg isotopic composition suggested that losses were strongly reaction-driven, although isotopic diagnostics did not conform to any known processes. Downstream of Y12, in the upper reach of the Lower EFPC, dissolved Hg fluxes tended to increase during the dormant season (net gain of 11-120%), and decrease during the growing season (net loss of 23% +/- 18%, 1SD). In the downstream-most reach of Lower EFPC, dissolved Hg fluxes increased by 12-108% in 9 out of 10 monthly assessments. Overall, diffuse fluxes from the non-Y12 watershed accounted for 34% (+/- 17%, 1SD) of all dissolved Hg exported during base flow. Within Lower EFPC, an assessment of Hg isotopic composition was consistent with the contribution of diffuse Hg inputs from high-concentration hotspots within riparian floodplains and streambed hyporheic pore water. To investigate remobilization of recalcitrant Hg from legacy sediment sources, we developed procedures that coupled isotopic analysis with sequential extractions of streambed sediment. We found that the proportion of weakly-bound Hg within EFPC streambed sediment was relatively small, but could still account for a large proportion of the annual flux of dissolved Hg from EFPC. These sequential extractions also showed that this weakly-bound Hg fraction could be replenished from the much larger fraction of recalcitrant Hg in sediment. The isotopic composition of these weakly-bound and remobilized recalcitrant Hg fractions within the sediment was consistent with high-concentration dissolved Hg hotspots within hyporheic pore water. Thus, this research provided novel evidence that legacy mercury sources within streambed sediment could provide an ongoing contribution of dissolved Hg to surface waters. Finally, we developed new methods for the direct determination of the MeHg isotopic composition of organisms, which allowed a more direct evaluation of inorganic Hg and MeHg sources accumulating in the food web. We found that fish and aquatic invertebrates in both EFPC and a regional background site obtained inorganic Hg and MeHg from multiple isotopically distinct sources, including sediment, suspended particulates, and periphyton. Photodemethylation was found to be an important reaction influencing MeHg dynamics at both sites. However, the balance of microbial methylation and demethylation processes differed between the two streams, with fractionation resulting from methylation and demethylation processes being relatively in balance within the regional background site, whereas microbial methylation appeared to be dominant over microbial demethylation within the EFPC ecosystem. Broadly, the application of Hg isotopic analysis in this study led to numerous novel insights regarding the biogeochemical cycling of Hg in stream ecosystems. This research project promoted the development of two new approaches, including the coupling of sequential extractions with Hg isotopic analysis to assess remobilization of recalcitrant Hg within sediments, and a new method for the isotopic analysis of MeHg isolated from environmental samples. Both of these efforts represent advances in capacity for the field of mercury isotopic analysis and environmental assessment. Overall, this study demonstrates that the application of Hg stable isotope techniques continues to provide new insights into the biogeochemical cycling of Hg in complex aquatic environments, both within the EFPC and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Sink Engineering in Photosynthetic Microbes

Photosynthetic organisms must balance harvested light energy (source) with a corresponding capacity to utilize the energy via downstream metabolic processes (sink). Insufficient source energy leads to starvation conditions, while insufficient sink capacity results in an overreduction of the electron transport chain, reduced photosynthetic efficiency, and photodamage. Despite the central importance of source/sink balancing in photosynthesis, our understanding of the mechanisms used to control this process in remains poor across diverse phototrophs. In particular, these balancing processes are essentially unstudied in cyanobacteria and microalgae, which possess distinct needs relative to the multicellular plants that have served as the model organisms for elucidating source/sink pathways. Recent bioengineering efforts have uncovered phenomena that are consistent with a well-developed source/sink sensing machinery in unicellular phototrophs. Furthermore, a deeper understanding of the fundamental mechanisms underlying source/sink dynamics in cyanobacteria and algae may prove to be critical for our capacity to boost photosynthetic efficiencies in these organisms, and enable their use as alternative crop species.

Carbon Fixation↗

DOE OSTI2017

Book Review: Lectures on BSDEs, Stochastic Control, and Stochastic Differential Games with Financial Applications by Rene Carmona

This book is a collection of lecture notes on various topics in the area of stochastic control, stochastic differential games, and financial applications. It makes for an excellent resource for those who already have some familiarity with these topics, although as the author points out, it is not a polished textbook. If the reader is not well versed in stochastic processes and control theory this book may not be the best introduction since prerequisite knowledge of these topics is a must before diving into the material. On the other hand, the book contains a very useful section of notes at the end of each chapter describing references that can be used to get up to speed and to put the material into the wider context. In this respect, it can be used as a guide for those interested in navigating the various books and papers that are drawn upon. The book is divided into three parts: Stochastic Calculus Preliminaries, Stochastic Control, and Stochastic Differential Games. The underlying theory is developed in a systematic manner and many financial applications are scattered throughout the book.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration

We perform a set of high-fidelity simulations of geochemical reactions within three-dimensional discrete fracture networks (DFN) and use various machine learning techniques to determine the primary factors controlling mineral dissolution. The DFN are partially filled with quartz that gradually dissolves until quasi-steady state conditions are reached. At this point, we measure the quartz remaining in each fracture within the domain as our primary quantity of interest. We observe that a primary sub-network of fractures exists, where the quartz has been fully dissolved out. This reduction in resistance to flow leads to increased flow channelization and reduced solute travel times. However, depending on the DFN topology and the rate of dissolution, we observe substantial variability in the volume of quartz remaining within fractures outside of the primary subnetwork. This variability indicates an interplay between the fracture network structure and geochemical reactions. We characterize the features controlling these processes by developing a machine learning framework to extract their relevant impact. Specifically, we use a combination of high-fidelity simulations with a graph-based approach to study geochemical reactive transport in a complex fracture network to determine the key features that control dissolution. We consider topological, geometric and hydrological features of the fracture network to predict the remaining quartz in quasi-steady state. We found that the dissolution reaction rate constant of quartz and the distance to the primary sub-network in the fracture network are the two most important features controlling the amount of quartz remaining. This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A nanocryotron memory and logic family

The development of superconducting electronics based on nanocryotrons has been limited so far to few device circuits, in part due to the lack of standard and robust logic cells. Here, we introduce and experimentally demonstrate designs for a set of nanocryotron-based building blocks that can be configured and combined to implement memory and logic functions. The devices were fabricated by patterning a single superconducting layer of niobium nitride and measured in liquid helium on a wide range of operating points. The tests show 10 - 4 bit error rates with above ± 20 % margins up to 50 MHz and the possibility of operating under the effect of an out-of-plane 36 mT magnetic field, with ± 30 % margins at 10 MHz. Additionally, we designed and measured an equivalent delay-flip-flop made of two memory cells to show the possibility of combining multiple building blocks to make larger circuits. These blocks may constitute a solid foundation for the development of nanocryotron logic circuits and finite-state machines with potential applications in the integrated processing and control of superconducting nanowire single-photon detectors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Practical Start-Up Process of Multiple Grid-Tied Voltage-Sourced Inverters in Laboratory

Starting up inverter-based resources (IBRs) and integrating them into a grid are important in real-world engineering and there are many challenges to be tackled. Using a laboratory-scale hardware test bed (50 Watt and 20 V), this paper demonstrates the challenges and provides a practical start-up process that can smoothly energize two grid-tied voltage-sourced inverters (VSI) and synchronize the VSIs into a 60-Hz grid. The hardware test bed consists of two SiC VSIs and a 45-kW Chroma grid simulator to act as the grid. The inverter controls are implemented in OPAL-RT OP5600, which has the measurements sensed, control algorithms processed, and gate signals generated and sent to the VSI switches. This research focuses on examining how to energize VSIs and how to synchronize the VSIs into the grid. The best energizing approach is summarized based on comparison and analysis of the experiment results. The presented approach can prevent large transients during the start-up process.

Wang, Zhengyu↗

DOE OSTI2020

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2025

Adapting Traditional Hazards Analysis Methods to Address Cyber Risks

Traditional hazards analysis (HA) methods, originally developed to address physical and operational risks, often fall short when it comes to identifying and mitigating cyber threats. These cyber threats pose unique and evolving risks to critical infrastructure and industrial control systems (ICS). This report explores the integration of Cyber-Informed Engineering (CIE) principles into existing HA methods to enhance their ability to address cyber-induced risks. CIE provides organizations with a practical, cost-effective approach to closing the gap between traditional HA methods and the need for cyber risk mitigation. By leveraging existing safety processes and controls, CIE allows users to examine and mitigate cyber vulnerabilities without overhauling existing HA methods. This report identifies areas where HA and CIE naturally align and where their approaches diverge. It emphasizes how CIE principles can be used to adapt HA methods, broadening their scope to include cyber risks and enabling the mitigation of cyber- induced impacts alongside traditional hazards and failure scenarios. This report examines how CIE can be applied across various HA methods—such as Hazard and Operability Studies (HAZOP), Probabilistic Risk Assessment (PRA), Failure Modes and Effects Analysis (FMEA), Systems-Theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), and Layers of Protection Analysis (LOPA). It provides strategies for integrating CIE to strengthen the identification, assessment, and mitigation of cyber-induced risks. The findings offer a structured entry point for organizations to embed CIE concepts into hazards and safety analyses, as well as broader engineering processes, ultimately supporting the design and operation of a more resilient infrastructure.

42 ENGINEERING↗

DOE OSTI2014

Survival Strategies in the Aquatic and Terrestrial World: The Impact of Second Messengers on Cyanobacterial Processes

Second messengers are intracellular substances regulated by specific external stimuli globally known as first messengers. Cells rely on second messengers to generate rapid responses to environmental changes and the importance of their roles is becoming increasingly realized in cellular signaling research. Cyanobacteria are photooxygenic bacteria that inhabit most of Earth’s environments. The ability of cyanobacteria to survive in ecologically diverse habitats is due to their capacity to adapt and respond to environmental changes. This article reviews known second messenger-controlled physiological processes in cyanobacteria. Second messengers used in these systems include the element calcium (Ca 2+ ), nucleotide-based guanosine tetraphosphate or pentaphosphate (ppGpp or pppGpp, represented as (p)ppGpp), cyclic adenosine 3’,5’-monophosphate (cAMP), cyclic dimeric GMP (c-di-GMP), cyclic guanosine 3’,5’-monophosphate (cGMP), and cyclic dimeric AMP (c-di-AMP), and the gaseous nitric oxide (NO). The discussion focuses on processes central to cyanobacteria, such as nitrogen fixation, light perception, photosynthesis-related processes, and gliding motility. In addition, we address future research trajectories needed to better understand the signaling networks and cross talk in the signaling pathways of these molecules in cyanobacteria. Second messengers have significant potential to be adapted as technological tools and we highlight possible novel and practical applications based on our understanding of these molecules and the signaling networks that they control.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Adapting Traditional Hazards Analysis Methods to Address Cyber Risks

42 - ENGINEERING↗

DOE OSTI2012

Modular control of fusion power heating applications

This work is motivated by the growing demand for auxiliary heating on small and large machines worldwide. Numerous present and planned RF experiments (EBW, Lower Hybrid, ICRF, and ECH) are increasingly complex systems. The operational challenges are indicative of a need for components of real-time control that can be implemented with a moderate amount of effort in a time- and cost-effective fashion. Such a system will improve experimental efficiency, enhance experimental quality, and expedite technological advancements. The modular architecture of this control-suite serves multiple purposes. It facilitates construction on various scales from single to multiple controller systems. It enables expandability of control from basic to complex via the addition of modules with varying functionalities. It simplifies the control implementation process by reducing layers of software and electronic development. While conceived with fusion applications in mind, this suite has the potential to serve a broad range of scientific and industrial applications. During the Phase-I research effort we established the overall feasibility of this modular control-suite concept. We developed the fundamental modules needed to implement open-loop active-control and demonstrated their use on a microwave power deposition experiment.

42 ENGINEERING↗

DOE OSTI2019

Determination of the structure-processing-properties relationship of Fe-6.5wt%Si

The role of Fe-6.5wt%Si as a soft magnetic material in motor and power generation will continue to increase as power and efficiency demands keep increasing due to high silicon steel’s superior magnetic and electrical properties when operated at high frequencies. However, the presence of brittle phases creates challenges for mass production of the materials and for machining the materials into parts with demanding geometries. Understanding the processing parameters controlling the presence of the brittle phases is necessary to promote high silicon steels as the superior soft magnetic material. This research pioneers the use of in-situ cooling rate thermal imaging analysis and a 2D Laue x-ray detector to quantify the relationship among processing parameters, final microstructure of melt-spun ribbons and the associated physical properties. These techniques reveal the superlattice peaks of the ordered B2/D03 phases separating them from the background A2 phase. Utilizing a variety of techniques investigating mechanical, electrical, and magnetic properties’ correlation to crystal structure alongside processing parameters a time temperature transformation curve can be generated.

01 COAL, LIGNITE, AND PEAT↗