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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Instrumentation and Control Digital Modernization Research Plan for Long-Term Sustainability in the Nuclear Industry

This report, developed by Idaho National Laboratory (INL) in collaboration with Oak Ridge National Laboratory (ORNL), outlines a comprehensive research plan to support the digital modernization of safety-related instrumentation and control (I&C) systems in the U.S. nuclear industry. The modernization effort is critical to ensuring the long-term safety, reliability, and economic viability of both existing and future nuclear power plants (NPPs), particularly as aging analog systems become increasingly obsolete and difficult to maintain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

New Insertion Device Control System for the APS Upgrade

New Hybrid Permanent Magnet Undulators (HPMUs) have been designed and manufactured using servo motors for precise and reliable gap motion control for the Advanced Photon Source Upgrade (APS-U) project. Meanwhile, existing HPMUs equipped with legacy stepper motors are systematically replaced with servo motors. In parallel with mechanical modifications of the undulators, a comprehensive upgrade has been implemented for the control of the devices. This upgrade includes integration of standardized industrial components for replacement of motor controllers and motor drives using the Kollmorgen Programmable Controller Multi-axis Master (PCMM) controllers and the AKD2G series servo drives. Soft Input Output Controllers (IOCs) are developed and deployed to replace the legacy VME-based IOCs for both single-period undulators and Revolver undulators. In this paper, we will present the architecture of the new insertion device control system, including control mechanisms, interlock protocols, and tools for diagnostics and troubleshooting.

Li, W.↗
DOE OSTI2022

Black carbon emissions and reduction potential in China: 2015–2050

Black carbon is a product of the incomplete combustion of carbonaceous fuels and has significant adverse effects on climate change, air quality, and human health. China has been a major contributor to global anthropogenic black carbon emissions. This study develops a black carbon inventory in China, using 2015 as the base year, and projects annual black carbon emissions in China for the period 2016–2050, under two scenarios: a Reference scenario and an Accelerated Reduction scenario. The study estimates that the total black carbon emissions in China in 2015 were 1100 thousand tons (kt), with residential use being the biggest contributor, accounting for more than half of the total black carbon emissions, followed by coke production, industry, agricultural waste burning, and transportation. This study then projects the total black carbon emissions in China in 2050 to be 278 kt in the Reference scenario and 86 kt in the Accelerated Reduction Scenario. Compared to the Reference scenario, the Accelerated Reduction scenario will achieve much faster and deeper black carbon reductions in all the sectors. The dramatic reductions can be attributed to the fuel switching in the residential sector, faster implementation of high-efficiency emission control measures in the industry, transportation, and coke production sectors, and faster phase-out of agricultural waste open burning. This analysis reveals the high potential of black carbon emission reductions across multiple sectors in China through the next thirty years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

National Ignition Facility. Facility and Infrastructure Systems Maintenance Plan

Ensuring the reliability of the NIF, including its support systems, laser systems, target diagnostic systems, and utilities, is essential to the availability of the NIF in its support of NNSA missions. NIF is a key capability in the DOE Stockpile Stewardship Program and supports high energy physics experiments for nuclear weapons, energy, and astrophysics applications. High system reliability provides opportunities for shots and scientific discoveries with opportunities to enhance and upgrade capabilities. This Maintenance Plan (MP) identifies the policies and procedures used to perform and support asset management of the NIF Facility and Infrastructure Systems (FInS), NIF Lasers & Alignment (LASE), NIF Target Experimental Operations (TOPS), NIF Target Area Science and Engineering (TASE), and NIF&PS Control Systems (NCS). The FInS systems include the facility, HVAC, contamination control, beampath, and Line Replaceable Units (LRUs) as well as utilities which create the beampath environments, such as vacuum, argon, or clean dry air. The LASE systems are Programmatic systems which include laser diagnostics, alignment, power conditioning, pulsed power, and input laser systems. The TOPS and TASE systems are also Programmatic systems which include target and diagnostic delivery systems and positioners, many different insertable and fixed target diagnostics, and cryogenic and target gas fill systems. Finally, the NCS systems include both software and hardware for industrial and shot operation control systems. Policies governing administrative and operational practices related to maintenance of FInS, LASE, TOPS, TASE, and NCS systems are described in this plan. In addition, the plan provides processes and procedures for managing, tracking, and documenting the work. This document, the NIF Operations Management Plan, NIF-5020544 (Ref. 1), and NIF Shot Operations Plan, NIF-5018506 (Ref. 2), together satisfy the requirements of the Conduct of Operations. Duties, responsibilities, and reporting requirements of the various positions associated with FInS, LASE, and TOPS maintenance are detailed in this plan. The FInS systems include both Real Property systems with asset management requirements specified in DOE Order 430.1C (Ref. 3) and Programmatic systems. In addition, for FInS, there is a list of the System Level Maintenance Plans (SLMPs) in NIF-1007419198 (Ref. 4) which provide the system descriptions and maintenance plan and schedule. In addition, the list includes the Reliability Centered Maintenance (RCM) and Experience Centered Maintenance (ECM) evaluations that have been performed for applicable FInS systems as well as reliability criticality per Section 3.5. The -AM version of the NIF Maintenance Plan focuses on the reliability program for FInS, LASE, TOPS, TASE, and NCS within the context of the overall NIF Reliability, Availability, and Maintainability (RAM) program and incorporates changes since the -AL version from August 2011 and has been updated to be fully consistent with the updates to Ref. 1. It also includes asset management considerations, updates to the Work Order (WO) process within the NIF Computerized Maintenance Management System (CMMS) which is EAM infor® System Maintenance and Reliability Tracking (SMaRT) (Ref. 5), and updates to metrics and key performance indicators (KPIs).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2022

Dynamic, resilient virtual sensing system and shadow controller for cyber-attack neutralization

An industrial asset may have monitoring nodes (e.g., sensor or actuator nodes) that generate current monitoring node values. An abnormality detection and localization computer may receive the series of current monitoring node values and output an indication of at least one abnormal monitoring node that is currently being attacked or experiencing a fault. An actor-critic platform may tune a dynamic, resilient state estimator for a sensor node and output tuning parameters for a controller that improve operation of the industrial asset during the current attack or fault. The actor-critic platform may include, for example, a dynamic, resilient state estimator, an actor model, and a critic model. According to some embodiments, a value function of the critic model is updated for each action of the actor model and each action of the actor model is evaluated by the critic model to update a policy of the actor-critic platform.

Roychowdhury, Subhrajit↗
DOE OSTI2022

Enhanced Control, Optimization, and Integration of Distributed Energy Applications (ECO-IDEA)

With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Xcel Energy, Schneider Electric, Varentec, and Electric Power Research Institute (EPRI) to meet the goals of the Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) program. This project developed and validated an innovative data-enhanced hierarchical control architecture that enables the efficient, reliable, resilient, and secure operation of future distribution systems with a high penetration of distributed energy resources like solar energy. The architecture enables a hybrid control approach where a centralized control layer is complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. It is fully interoperable and includes all the cybersecurity aspects necessary for reliable and secure system operation. The hybrid approach can seamlessly integrate multiple voltage-regulation technologies, both at central and grid-edge levels, which enables reliable and efficient system operation in the face of unpredictable conditions. The overarching goal of the Eco-Idea project is to develop, validate, and deploy a unique and innovative Data-Enhanced Hierarchical Control (DEHC) architecture that comprehensively addresses the formidable challenges associated with proliferation of high penetration of distributed PV such as reverse power flows, transients from variability of PV systems, feeder load balancing, and voltage stability. These issues are exposing the weaknesses of existing grid operations and controls - including, but not limited to, lack of grid situational awareness, heuristic and slow-acting control actions, latency of control for emergency situations, and points of failure in communications. The proposed architecture will comprehensively resolve the deficiencies of current operational settings - where monitoring and control solutions proposed across industry and academia may not be interoperable and may not coexist in the same system - and will enable an efficient, reliable, resilient, and secure operation of future distribution systems with penetration of solar energy well beyond current limits. The DEHC architecture was developed and validated rigorously through hardware-in-loop simulations in the laboratory environment and deployed on the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Feature Classification for Control System Devices

Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device’s memory.

ahmed mithu, M Rayhan↗
DOE OSTI2023

A reacting multiphase computational flow model for 2,3-butanediol synthesis in industrial-scale bioreactors

The microbe, Zymomonas mobilis, can efficiently convert sugars to 2,3 butanediol (BDO), an important intermediate for downstream chemical products, only in a well-controlled microaerated environment. However, controlling oxygen distribution in industrial-scale bioreactors is challenging, and at-scale BDO production is hard to optimize using traditional engineering methods. This study takes a step towards addressing this problem through a computational model for reacting multiphase flows in large-scale bioreactors. A phenomenological metabolic model was first developed and validated against experiments, and then it was coupled to a multiphase computational fluid dynamics (CFD) solver using a subcycling algorithm for simulating long conversion times (~30 hours). Large-scale (~500 m 3 ) bubble column simulations using this coupled approach demonstrated a 25% improvement in BDO yield with low sparging rates (0.002 m/s) and low height-to-diameter ratio (0.875) compared to a baseline bubble column (35 m height, 5 m diameter) with a superficial gas velocity of 0.06 m/s.

09 BIOMASS FUELS↗
DOE OSTI2024

Sustainability in Directed Energy Deposition

Directed energy deposition (DED) additive manufacturing (AM) has been recognized as an efficient and sustainable technology in the field of advanced manufacturing. In the past few years, Considerable discussion has been made by researchers to promote DED AM for better performance in manufacturing. The discussions focus on basic theoretical research, process optimization and control, technology innovation, and industrial applications. However, this technology׳s environmental and economic benefits over traditional manufacturing processes are still to be seen and its sustainability is also a mystery. Here, this chapter presents a critical overview of the environmental sustainability of DED to provide a comprehensive understanding of the effects of materials, processing conditions, and geometric complexities on the energy consumption and environmental impacts of DED and provide a better guide for decision-makers in terms of selecting suitable manufacturing processes. In addition, this chapter proposes a novel framework for DED AM sustainability assessment and improvement based on the Life Cycle Assessment (LCA) method and multi-objective optimization. At last, future perspectives and recommendations for the sustainability of DED AM are put forward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Vapor–liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks

Understanding fluid phase behavior, like VLE, in high P&T conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fueled combustion systems and also forms an integral part of the design-modeling of the control processes in chemical industries. Two data-driven models have been proposed here in this study, each of which was competent in estimating VLE for the Type III binary systems of C 10 /N 2 and C 12 /N 2 , at pressures ranging up to 50–60 MPa. Both models showed better performance in predicting equilibrium pressure as compared to VLE modeled using PR-EOS. A modified model has also been proposed, capable of estimating the full phase envelope for the binary systems of C 10 /N 2 and C 12 /N 2 across a wide range of temperatures, and thus exhibit the mixture critical pressure at the concerned temperature. The diverse applicability of the proposed network architecture was further exhibited while estimating the VLE of a ternary system of C 1 /C 10 /N 2 .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Towards the applications of mechanophore incorporated feedstocks for additive manufacturing

The ability to additively-manufacture mechanically responsive molecules, known as mechanophores (MPs), that are incorporated into polymer feedstocks provides opportunities for self-healing, real-time damage detection, and improvements in quality assurance and control capabilities to several industries (wind energy technology, building and construction, etc.) who are adopting additive manufacturing (AM). However, before the applications are realized and industrially adopted, further research and development regarding MP-incorporated AM feedstock availability, production scale-up, and processability and printability is needed. Here, the goal of this review is to bridge the gap between the bench top and real world applications of AM of MPs by identifying high impact application spaces and highlighting the challenges that need to be overcome for widespread adoption. The state-of-the-art of AM of MP-incorporated feedstocks is reviewed, followed by a discussion of potential future applications, current challenges, and research areas that work toward commercialization of AM of MP-incorporated feedstocks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Designing a Nanoscale Three-phase Electrochemical Pathway to Promote Pt-catalyzed Formaldehyde Oxidation

Gas-phase heterogeneous catalysis is a process spatially constrained on the two-dimensional surface of a solid catalyst. Here, we introduce a new toolkit to open up the third dimension. We discovered that the activity of a solid catalyst can be dramatically promoted by covering its surface with a nanoscale-thin layer of liquid electrolyte while maintaining efficient delivery of gas reactants, a strategy we call three-phase catalysis. Introducing the liquid electrolyte converts the original surface catalytic reaction into an electrochemical pathway with mass transfer facilitated by free ions in a three-dimensional space. Furthermore, we chose the oxidation of formaldehyde as a model reaction and observed a 25000-times enhancement in the turnover frequency of Pt in three-phase catalysis as compared to conventional heterogeneous catalysis. We envision three-phase catalysis as a new dimension for catalyst design and anticipate its applications in more chemical reactions from pollution control to the petrochemical industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An 860 MHz Wireless Surface Acoustic Wave Sensor with a Metal-Organic Framework Sensing Layer for CO2 and CH4

Wireless and passive surface acoustic wave (SAW) devices with nanoporous metal-organic framework (MOF) sensing layers are attractive gas sensors for applications in many fields such as energy industries and air pollution control. Here, we report on enhancing the sensitivity and detection limit of zeolitic imidazolate framework-8 (ZIF-8) MOF-coated SAW reflective delay line mass sensors by increasing the operating frequency for sensitive detection of carbon dioxide (CO2) and methane (CH4) at ambient conditions. In particular, we show at least four times higher sensitivity of an 860 MHz (4-μm periodicity) SAW reflective delay line coated with a 240 nm thick ZIF-8 compared to the sensitivity of a 430 MHz (8-μm periodicity) otherwise identical sensor device to the targeted gases. The detection limits of the higher frequency wireless devices for CO2 and CH4 were estimated to be 0.91 vol-% and 7.01 vol-%, respectively. The enhanced sensitivity for higher frequency devices is explained in terms of the frequency dependent acoustic wave energy confinement.

Devkota, Jagannath↗
DOE OSTI2024

Reaction engineering: a lost (and found) art for a decarbonized future [Slides]

The heart (or, perhaps more appropriately, the stomach) of any chemical manufacturing process is the reactor, where chemical and physical transformations occur under precisely controlled conditions. For traditional industrial processes, the design, optimization and operation of chemical reactors are extremely well developed, drawing upon the principles of chemistry, physics, calculus and economics. However, new-age process concepts proposing to use "exotic" reactors and/or sustainable inputs (e.g., renewable carbon resources, electrons, photons, plasma) face a dearth of well documented reaction engineering principles & design practices that eclipses their rapid adoption. This presentation features two vignettes of (the return of) reaction engineering in sustainable process science, first in the valorization of lignin from biomass, and second in understanding multimodal catalyst deactivation in industrial-scale reactors within the Catofin process for propane dehydrogenation. Overall, this seminar seeks to educate student researchers on practical means of advancing laboratory reactor technologies towards commercial practice.

09 BIOMASS FUELS↗
DOE OSTI2020

U.S. Electronic Neutron Generator Use in Research and Academia – A Survey [Slides]

This is a slide deck intended for use at the upcoming IAEA workshop "EVT2002488 Consultancy Meeting on Industrial Applications of Nucleonic Control Systems based on Neutrons from sealed sources and generators". This slide deck provides a survey of U.S. commercial neutron generator technology, including manufacturers, and a few examples of use in academia, and some prior research at INL. All data and figures in this presentation are either publicly available (note citations) or are INL materials that have been previously release through STISM for public presentations or papers.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Advancing Conduction-Cooled 650 MHz SRF Technology for Industrial Accelerators at Fermilab's IARC

The National Nuclear Security Administration (NNSA) funds the Illinois Accelerator Research Center (IARC) at Fermilab in developing a high-power, conduction-cooled Superconducting Radio Frequency (SRF) accelerator tailored for industrial applications requiring robust and efficient operation. A 650 MHz, 1.6 MeV, 20 kW SRF accelerator is currently under development, employing a conduction cooling approach to simplify cryogenic requirements and enhance accessibility for industrial use. The accelerator’s control system is implemented on the Blinky Lite platform, selected for its open-source architecture, secure remote access capabilities, and operational flexibility—attributes advantageous for industrial deployment and sustained operation. A dedicated beamline is designed to measure essential beam parameters and test the integrated performance of the accelerator and control systems, thereby validating their operational readiness for intended applications

Ji, Y. [Fermilab] (ORCID:0000000233981752)↗