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DOE OSTI2014

Estimating Power System Dynamic States Using Extended Kalman Filter

Abstract—The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This new dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.

Huang, Zhenyu↗

DOE OSTI2024

Cytrics Repository Of Analysis Tools And Engineering Resources

Cybersecurity Testing for Resilient Industrial Control Systems (CyTRICS) is a DOE-funded project that works with vendors to evaluate the cybersecurity of equipment used in US critical infrastructure. In the process of testing systems, CyTRICS researchers often develop custom tools. The tools in this repository were developed during multiple CyTRICS tests to assist with the testing process. They help solve problems encountered by CyTRICS researchers and address uncommon testing subjects for which limited tooling is available. They are useful to other researchers working on similar systems and architectures.

Laird, SutterE↗

DOE OSTI2022

Generalized large optics fabrication multiplexing

High precision astronomical optics are manufactured through deterministic computer controlled optical surfacing processes, such as subaperture small tool polishing, magnetorheological finishing, bonnet tool polishing, and ion beam figuring. Due to the small tool size and the corresponding tool influence function, large optics fabrication is a highly time-consuming process. The framework of multiplexed figuring runs for the simultaneous use of two or more tools is presented. This multiplexing process increases the manufacturing efficiency and reduces the overall cost using parallelized subaperture tools.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Quantification of UV-Visible and Laser Spectroscopic Techniques for Materials Accountability and Process Control

Ultraviolet–visible spectroscopy (UV–Visible) and time-resolved laser fluorescence spectroscopy (TRLFS) optical techniques can permit on-line analysis of actinide elements in a solvent extraction process in real time. These techniques have been used for measuring actinide speciation and concentration under laboratory conditions and are easily adaptable to multiple sampling geometries, such as dip probes, fiber-optic sample cells, and flow-through cell geometries. To fully exploit these techniques, researchers must determine the fundamental speciation of target actinides and the resulting influence on spectroscopic properties. Detection limits, process conditions, and speciation of key actinide components can be established and utilized in a range of areas, particularly those related to materials accountability and process control. Through this project, researchers will develop tools and spectroscopic techniques to evaluate solution extraction conditions and concentrations of U, Pu, and Cm in extraction processes, addressing areas of process control and materials accountability. The team will evaluate UV– Visible and TRLFS for use in solvent extraction-based separations. Ongoing research is examining efficacy of UV-Visible spectroscopy to evaluate uranium and plutonium speciation under conditions found in the UREX process and using TRLFS to evaluate Cm speciation and concentration in the TALSPEAK process. A uranyl and plutonium nitrate UV–Visible spectroscopy study met with success, which supports the utility and continued exploration of spectroscopic methods for evaluation of actinide concentrations and solution conditions for other aspects of the UREX+ solvent extraction scheme. This project will examine U and Pu absorbance in TRUEX and TALSPEAK, perform detailed examination of Cm in TRUEX and TALSPEAK, study U laser fluorescence, and apply project data to contactors. The team will also determine peak ratios as a function of solution concentrations for the UV-Visible spectroscopy studies. The use of TRLFS to examine Cm and U will provide data to evaluate lifetime, peak location, and peak ratios (mainly for U). The bases for the spectroscopic techniques have been investigated, providing fundamental evidence for the application’s utility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

The combined application of the anti- RAS1 and anti- RIM101 2'- O MethylRNA oligomers enhances Candida albicans filamentation control

Abstract Although antisense oligomers (ASOs) have been successfully utilized to control gene expression, they have been little exploited to control Candida albicans virulence's determinants. Filamentation is an important virulence factor of C. albicans, and RAS1 and RIM101 genes are involved in its regulation. Thus, the main goal of this work was to project ASOs, based on 2'-OMethyl chemical modification, to target RAS1 and RIM101 mRNA and to validate its application either alone or in combination, to reduce Candida filamentation in different human body fluids. It was verified that both, anti-RAS1 2'OMe and anti-RIM101 2'OMe oligomers, were able to reduce the levels of RAS1 and RIM101 genes’ expression and to significantly reduce C. albicans filamentation. Furthermore, the combined application of anti-RAS1 2'OMe oligomer and anti-RIM101 2'OMe oligomer enhances the control of C. albicans filamentation in artificial saliva and urine. Our work confirms that ASOs are useful tools for research and therapeutic development on the control of candidiasis.

Barbosa, Ana↗

DOE OSTI2013

RAVEN: Dynamic Event Tree Approach Level III Milestone

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

RAVEN. Dynamic Event Tree Approach Level III Milestone

42 ENGINEERING↗

DOE OSTI2013

Dynamic Event Tree Analysis Through RAVEN

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

42 ENGINEERING↗

DOE OSTI2015

Electrical control of second-harmonic generation in a WSe 2 monolayer transistor

Nonlinear optical frequency conversion, in which optical fields interact with a nonlinear medium to produce new field frequencies, is ubiquitous in modern photonic systems. However, the nonlinear electric susceptibilities that give rise to such phenomena are often challenging to tune in a given material and, so far, dynamical control of optical nonlinearities remains confined to research laboratories as a spectroscopic tool. In this paper, we report a mechanism to electrically control second-order optical nonlinearities in monolayer WSe 2 , an atomically thin semiconductor. We show that the intensity of second-harmonic generation at the A-exciton resonance is tunable by over an order of magnitude at low temperature and nearly a factor of four at room temperature through electrostatic doping in a field-effect transistor. Such tunability arises from the strong exciton charging effects in monolayer semiconductors, which allow for exceptional control over the oscillator strengths at the exciton and trion resonances. The exciton-enhanced second-harmonic generation is counter-circularly polarized to the excitation laser due to the combination of the two-photon and one-photon valley selection rules, which have opposite helicity in the monolayer. Finally, our study paves the way towards a new platform for chip-scale, electrically tunable nonlinear optical devices based on two-dimensional semiconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2014

High-precision micro/nano-scale machining system

A high precision micro/nanoscale machining system. A multi-axis movement machine provides relative movement along multiple axes between a workpiece and a tool holder. A cutting tool is disposed on a flexible cantilever held by the tool holder, the tool holder being movable to provide at least two of the axes to set the angle and distance of the cutting tool relative to the workpiece. A feedback control system uses measurement of deflection of the cantilever during cutting to maintain a desired cantilever deflection and hence a desired load on the cutting tool.

42 ENGINEERING↗

DOE OSTI2024

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Induced protein expression in Leptospira spp. and its application to CRISPR/Cas9 mutant generation

Abstract Expanding the genetic toolkit for Leptospira spp. is a crucial step toward advancing our understanding of the biology and virulence of these atypical bacteria. Pathogenic Leptospiraare responsible for over 1 million human leptospirosis cases annually and significantly impact domestic animals. Bovine leptospirosis causes substantial financial losses due to abortion, stillbirths, and suboptimal reproductive performance. The advent of the CRISPR/Cas9 system has marked a turning point in genetic manipulation, with applications across multiple Leptospira species. However, incorporating controlled protein expression into existing genetic tools could further expand their utility. We developed and demonstrated the functionality of IPTG-inducible heterologous protein expression in Leptospira spp. This system was applied for regulated expression of dead Cas9 (dCas9) to generate knockdown mutants, and Cas9 to produce knockout mutants by inducing double-strand breaks (DSB) into desired targets. IPTG-induced dCas9 expression enabled validation of essential genes and non-coding RNAs. Additionally, IPTG-controlled Cas9 expression combined with a constitutive non-homologous end-joining (NHEJ) system allowed for successful recovery of knockout mutants, even in the absence of IPTG. These newly controlled protein expression systems will advance studies on the basic biology and virulence ofLeptospira, as well as facilitate knockout mutant generation for improved veterinary vaccines.

Science & Technology - Other Topics↗

DOE OSTI2020

A Tool for Performing Link Analysis, Operational Sequence Analysis, and Workload Analysis to Support Nuclear Power Plant Control Room Modernization

Nuclear power continues to have an imperative role for the U.S.’s electricity generation. However, for these nuclear power plants (NPPs) to remain economically viable, new strategies for reducing operations and maintenance costs must be explored. The U.S. Department of Energy Light Water Reactor Sustainability Program is researching how to digitally transform existing analog and hybrid main control rooms into a fully integrated control room that addresses this challenge. The transformation will fundamentally change the conduct of operations for these U.S. NPPs. Human factors engineering has a vital role in this effort, where traditional task analysis methods are important in informing the design of advanced human-system interface displays. This work describes a preliminary tool to support task analysis for the development of these advanced displays. Details on the use of this tool, including the specific task analysis methods offered, are presented in this paper.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2015

Characteristic operator functions for quantum input-plant-output models and coherent control

We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the characterization of quantum feedback control systems that fits in with the general theory of networks. The definition exploits the linearity of noise differentials in both the plant Heisenberg equations of motion and the differential form of the input-output relations. Mathematically, the characteristic operator is a matrix of dimension equal to the number of outputs times the number of inputs (which must coincide), but with entries that are operators of the plant system. In this sense, the characteristic operator retains details of the effective plant dynamical structure and is an essentially quantum object. We illustrate the relevance to model reduction and simplification definition by showing that the convergence of the characteristic operator in adiabatic elimination limit models requires the same conditions and assumptions appearing in the work on limit quantum stochastic differential theorems of Bouten and Silberfarb [Commun. Math. Phys. 283, 491-505 (2008)]. This approach also shows in a natural way that the limit coefficients of the quantum stochastic differential equations in adiabatic elimination problems arise algebraically as Schur complements and amounts to a model reduction where the fast degrees of freedom are decoupled from the slow ones and eliminated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

U.S.-China Clean Energy Research Center Building Energy Efficiency (CERC-BEE) Open-Source Retrofit Targeting Tool (CRADA FP00007338 Final Report)

To increase the cost-saving energy and carbon dioxide (CO 2 ) emissions reductions in buildings and portfolios at the scale and speed necessary to limit climate change, researchers at LBNL and Johnson Controls (JCI) developed the Building Efficiency Targeting Tool for Energy Retrofits (BETTER). BETTER is a software tool that consists of three components: (1) the BETTER analytical engine source code (which was developed with intellectual property provided by JCI under CRADA FP00007338); (2) the BETTER web application, developed by LBNL and McQuillen Interactive Pty. Ltd; and (3) the BETTER application programming interface (API), also developed by LBNL and McQuillen Interactive Pty. Ltd. BETTER enables building and portfolio owners, managers, and service providers worldwide to quickly, easily identify cost-saving energy efficiency retrofits in existing buildings and portfolios without expensive site visits or complex modeling. With minimal data input, the tool benchmarks a building’s electric and fossil energy usage against peers; quantifies energy, cost and greenhouse gas (GHG) emission reduction potentials at the building and portfolio levels; and recommends energy efficiency measures to decarbonize and electrify buildings and portfolios, targeting specific energy savings levels. No other tool so comprehensively analyzes buildings and portfolios with such ease. If fully implemented, it is estimated that BETTER could help reduce emissions equivalent to planting 1.3 billion trees globally by 2030. Moreover, an additional 50-75% of embodied GHG emissions could be avoided in each case where BETTER results in a building being retrofitted instead of demolished and replaced, providing substantial additional decarbonization benefits for the buildings sector. BETTER has garnered multiple awards and avid interest from investors. In 2020, it earned a R&D 100 Award for innovation and a LBNL Director’s Award for Technology Transfer. In 2021, BETTER was named an EarthX E-Capital Summit Climate Tech Prize semi-finalist

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

The Usage of Error Compensation Tools of CNC for Vertical Milling Machines

The error of metal-cutting machines must be kept within standard limits when a numerical control system is first introduced. A mechanism for more precise control of the motion is proposed on the basis of control modules in the core of the CNC. The results of using the proposed approach when the AxiOMA Control numerical control system is introduced at the Quaser MV184 machine tool (produced by “KEMZ” Ltd.) are outlined.

42 ENGINEERING↗

DOE OSTI2025

Engineering microalgal cell wall-anchored proteins using GP1 PPSPX motifs and releasing with intein-mediated fusion

AbstractHarnessing and controlling the localization of recombinant proteins is critical for advancing applications in synthetic biology, industrial biotechnology, and drug delivery. This study explores protein anchoring and controlled release inChlamydomonas reinhardtii, providing innovative tools for these fields. Using truncated variants of the GP1 glycoprotein fused to the plastic-degrading enzyme PHL7, we identified the PPSPX motif as essential for anchoring proteins to the cell wall. Constructs with increased PPSPX content exhibited reduced secretion but improved anchoring, pinpointing the potential anchor-signal sites of GP1 and highlighting the distinct roles of these motifs in protein localization. Building on the anchoring capabilities established with these glycomodules, we also demonstrated a controlled release system using a pH-sensitive intein derived from RecA fromMycobacterium tuberculosis. This intein efficiently cleaved and released PHL7 and mCherry that was fused to GP1 under acidic conditions, enabling precise temporal and environmental control. At pH 5.5, fluorescence kinetics demonstrated significant mCherry release from the pJPW4mCherry construct within 4 hours. In contrast, release was minimal under pH 8.0 conditions and negligible for the pJPW2mCherry (W2) control, irrespective of the pH. Additionally, bands on the Western blot at the expected size of mCherry also showed its efficient release from the mCherry::intein::GP1 fusion protein at pH 5.5. Conversely, at pH 8.0, no bands were detected. This anchor-release approach offers significant potential for drug delivery, biocatalysis, and environmental monitoring applications. By integrating glycomodules and pH-sensitive inteins, this study establishes a versatile framework for optimizing protein localization and release inC. reinhardtii, with broad implications for proteomics, biofilm engineering, and scalable therapeutic delivery systems.Graphical Abstract

Kang, Kalisa (ORCID:0009000619398129)↗

DOE OSTI2026

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco↗