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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Low-frequency Electromagnetic Detection Limits of Underground Nuclear Explosions

Underground nuclear explosions produce a broadband electromagnetic signal that can propagate to the surface at some distance. We perform a logistic regression on historical electromagnetic detections to calculate a probabilistic detection curve for prediction of the propagation distance for any given underground nuclear explosion. The curve predicts positive detections at scaled ranges of 2414.6, 2025.7, 1908.3, and 1672.4 m/ktonne 1/3 at probabilities of 50, 90, 95 and 99%, respectively. For a theoretical 100 ktonne underground nuclear explosion these scaled ranges translate to absolute ranges of 11.2, 9.4, 8.9, and 7.8 km at probabilities of 50, 90, 95 and 99%, respectively. We also motivate a need for improved electromagnetic sensor design and deployment by extending the detection analysis using the quoted signal-to-noise ratio of two and noise amplitude of 10 pT along with attenuation relationships that go as 1/r to 1/r 3 . We find that reducing the noise floor by a factor often could extend the detection limit to approximately 5 to 25 km/ktonne 1/3 , which translates to 24 to 112 km for a theoretical 100 ktonne underground nuclear explosion.

58 GEOSCIENCES↗
DOE OSTI2021

Next Generation Logistics Systems for Delivering Optimal Biomass Feedstocks to Biorefining Industries in the Southeastern U.S.

The diverse portfolio of biomass sources that is available in the Southeastern U.S., including a significant supply of pine “residue”, represents a valuable strategic position for the region. Through blends formulated based on critical properties, this project will take full advantage of the range in biomass properties afforded by the portfolio to produce a consistent, high-performance feedstock for the industry, while lowering cost. Key developments being targeted to enable this potential include whole-tree transport to a state-of-the-art merchandising depot that will further access biomass from ongoing, forest industry operations. The approach will more effectively utilize the tree and distribute cost, while minimizing in-woods contamination of the woody biomass component. To implement this vision, information on the chemical composition and changes that are induced during multiple preprocessing steps (size reduction, moisture removal, densification, etc.) is needed. New NIR sensor technology will be developed for online monitoring of important biomass properties. The data will be incorporated into a statistical process control platform to improve process efficiency and meet required specifications. Advanced process models are being developed to inform the techno-economic and life-cycle assessment of the program’s impact. The new system will ultimately reduce operational risks from supply chain disruptions, and allow operation of larger-scale biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Analysis of temperature effects on self-powered neutron detectors in a gamma field

Self-powered neutron detectors (SPND) are small, robust, in-core radiation sensors that have the potential to operate in high temperature environments (above 600°C). However, previous experiments indicated temperature related effects in the form of displacement currents. Given the recent irradiation at The Ohio State University Research Reactor using a delayed-response rhodium-based SPND alongside a prompt-response tantalum-based SPND, it was concluded that only low-energy electrons from gamma interactions were the cause of these temperature effects. A series of heated gamma irradiations with temperatures up to 750°C of SPNDs were performed at the Advanced Test Reactor Gamma Facility in order to better characterize these displacement currents. The results from the experiments demonstrated a strong relation between the displacement currents that occurred during temperature ramps to thermoluminescence (TL) glow peaks—the SPND with Al2O3 insulation has one glow peak within 750°C and the SPND with MgO has three. A demonstration was performed to calculate the trap energy levels of the displacement currents through TL glow peak analysis. Unfortunately, the methods employed provided inconsistent results, requiring an updated model based on ongoing irradiation instead of post-irradiation measurements.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Quality concerns caused by quality control — deformation of silicon strip detector modules in thermal cycling tests

The ATLAS experiment at the Large Hadron Collider (LHC) is currently preparing to replace its present Inner Detector (ID) with the upgraded, all-silicon Inner Tracker (ITk) for its High-Luminosity upgrade (HL-LHC). The ITk will consist of a central pixel tracker and the outer strip tracker, consisting of about 19,000 strip detector modules. Each strip module is assembled from up to two sensors, and up to five flexes (depending on its geometry) in a series of gluing, wirebonding and quality control steps. During detector operation, modules will be cooled down to temperatures of about -35 °C (corresponding to the temperature of the support structures on which they will be mounted) after being initially assembled and stored at room temperature. In order to ensure compatibility with the detector's operating temperature range, modules are subjected to thermal cycling as part of their quality control process. Ten cycles between -35 °C and +40 °C are performed for each module, with full electrical characterisation tests at each high and low temperature point. As part of an investigation into the stress experienced by modules during cooling, it was observed that modules generally showed a change in module shape before and after thermal cycling. This paper presents a summary of the discovery and understanding of the observed changes, connecting them with excess module stress, as well as the resulting modifications to the module thermal cycling procedure.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Solar Transposition Modeling via Deep Neural Networks With Sky Images

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180° arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N -dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. Furthermore, these profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.

42 ENGINEERING↗
DOE OSTI2022

Intensity Modulated Fiber Optic Sensor: A Novel Grid Measurement Unit

This paper presents a novel approach to physical displacement-based power grid measuring via an Intensity Modulated Fiber Optic Sensor (IMFOS). An IMFOS utilizes one fiber to transmit the intensity modulation light from its Electro-Optic controller to a fiber optic probe. The power grid voltage and current can induce physical displacements in transducers via the piezoelectric effect and the Lorentz law, respectively, which then result in a distance change between the optical probe and the reflective surface of the transducers. In parallel, multiple fibers are used to collect the reflective light for electro-optic conversion. Furthermore, a National Instruments-based characterization platform is set up for performance evaluation. Here, the testing result demonstrates that the IMFOS is immune to the inherent DC and low-frequency saturation issues prevalent in conventional potential and current transformers. Finally, the IMFOS is implemented in a Universal Grid Analyzer to illustrate its applicability for phasor estimation in actual power grids.

42 ENGINEERING↗
DOE OSTI2023

A Case Study of Tunable White LED Lighting with Networked Lighting Controls (Emory University Cognitive Empowerment Program)

Emory University and Georgia Institute of Technology (Georgia Tech) partnered to create the Charlie and Harriet Schaffer Cognitive Empowerment Program (CEP) facility in northeast Atlanta. Together with the funders they are building a program to help individuals experiencing mild cognitive impairment (MCI) to maintain their physical and cognitive health, and independence as long as possible. In addition to applying effective strategies and therapies, the two research groups are investigating the responses of the MCI members to treatments that involve acoustical conditions, exercise and movement, and lighting changes that may support retention or relearning of skills. Care partners and family members receive support and instruction to improve home and work life, promoting joy, purpose, and wellness in the family groups. The lighting system uses tunable-white LEDs, employing luminaires with both warm and cool-color emitters that can be dimmed separately to produce any white correlated color temperature (CCT) between 2700 K and 6500 K. All luminaires were dimmable to achieve subdued or lively surroundings for different treatments, time-of-day, and mood. A central networked digital control system was employed to allow tuning of multiple spaces together (for example, bright, cool morning light could be programmed for extra stimulation, or lighting in all spaces at the end of the day could be reduced in both light output and CCT to promote relaxation and not interfere with the melatonin cycle of occupants). Almost all spaces were equipped with individual room control of dimming and color temperature with touch screens to allow users to tune the lighting as desired, but each room’s controls could also be specially programmed through the server in case the research staff were investigating lighting settings on learning, for example. The server incorporates a timeclock, and it is able to send signals to switch off all lighting after occupancy hours, or enable occupancy sensors to control the lighting. The bulk of the construction work was completed in January 2020. It was clear from an initial walk-through that although the lighting system produced the expected high light level with low-glare qualities of light, that there were issues and inconsistencies to be resolved. These were noted in an initial punchlist visit and expected to be resolved when the tech representatives from the agency visited with the electrical contractor in the following few weeks. What followed was 2.5 years of identifying unexpected lighting performance in terms of light output, color, scheduling, and occupancy. This report documents the issues encountered in the effort to get the lighting and controls systems to operate as intended. It concludes with guidance for design professionals and manufacturers to help avoid problematic complexity in future projects.

42 ENGINEERING↗
DOE OSTI2024

Evaluation of In-Situ AM Process Monitoring Techniques and Potential for Detecting Process Anomalies and Undesirable Microstructures

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing rapid qualification of new materials for fabrication of nuclear relevant components using advanced manufacturing techniques. Particular interest is placed on code-qualifying stainless steel (SS) 316H processed by laser powder bed fusion (LPBF) additive manufacturing. A paradigm that incorporates data from in-situ sensing during the printing, ex-situ characterization, and advanced artificial intelligence–based models was established under the Transformation Challenge Reactor (TCR) program to develop a pedigree for each fabricated component that could be tracked from the feedstock to the component’s release for application. Under the TCR program, the Peregrine software was developed as a tool for incorporating the vast amounts of in-situ and ex-situ characterization data collected; all data stored on a rapidly growing digital platform. The digital platform allows for users to link site-specific process anomalies to the macro- and microstructure. The platform will eventually be able to predict component performance, which will be crucial to qualifying materials and components in risk-averse industries such as those supporting and building nuclear reactors. Current in-situ process monitoring techniques that are already integrated with software like Peregrine are advantageous for identifying process anomalies including powder spatter, component edge swelling, recoating-build interactions, and so on. However, additional data are required to fully predict the resulting microstructures needed for identifying relationships to component performance. The rapid cooling rates observed in LPBF are some of the highest of any bulk manufacturing process, resulting in heterogenous microstructures and typically causing anisotropy in mechanical properties. Moreover, evolved residual thermal stresses are high, which can cause severe defects such as delamination or cracking. Therefore, other in-situ monitoring methods are warranted for exploration to measure and map the thermal history, and potentially the stress state, of each build. This report summarizes different in-situ monitoring strategies proposed for LPBF with a focus on the more developed sensor systems. Novel capabilities for measuring melt pool temperatures are also addressed to better inform modeling efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2023

High-temperature irradiation-resistant thermocouple instability model for in-pile reactor use

This article presents an instability model for the high-temperature irradiation-resistant thermocouple (HTIR-TC). Here the term instability defines the superposition of both drift and inhomogeneity of TC thermoelements occurring simultaneously. The HTIR-TC is an advanced thermocouple (TC) that uses the refractory metals niobium and molybdenum as sensing thermoelements for generating electromotive force (EMF) in a field of neutrons and at temperatures upward of 1,600°C. In the Advanced Gas Reactor (AGR) 5/6/7 tests conducted at Idaho National Laboratory’s Advanced Test Reactor (ATR), the HTIR-TCs showed low to moderate instability throughout the life of the test. The instability model reveals that HTIR-TCs can, when the operating temperature of the reactor fuel is normal, maintain performance throughout an 18-month refueling cycle typical of nuclear power plants, reflecting an instability of less than ±1%. The HTIR-TC is also qualified for incorporation into a test fixture during the testing of new fuels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

High performance, rapid thermal/UV curing epoxy resin for additive manufacturing of short and continuous carbon fiber epoxy composites

An additive manufacturing resin system includes a container; a build platform in the container; a build platform drive; an additive manufacturing resin that forms an additive manufacturing resin bath; an additive manufacturing print head; a build material supply connected to the additive manufacturing print head; an infill resin in the container, wherein the infill resin is supported by the additive manufacturing resin bath; a computer controller; and an extruded material that forms the product, wherein the infill resin at least partially surrounds the extruded material that forms the product. An infill resin supply provides the infill resin into the container and a control valve is connected to the infill resin supply. An IR light sensor is connected to the control valve and operatively connected to the infill resin in the container.

Lewicki, James↗
DOE OSTI2021

Protein conformational switch discerned via network centrality properties

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensembleinformed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

First results from a high-frame-rate, multi-GHz ionizing particle detection system geared toward accelerator diagnostic applications

An integrated detection system, consists of a thin diamond sensor incorporated into a compact signal loop, coupled in turn to an application-specific integrated circuit readout chip, has been tested for the first time, using ps-duration electron pulses from the Next Linear Collider Test Accelerator at the SLAC National Accelerator Laboratory. For readout frame rates as high as 120 kHz, the detection system is shown to achieve a linear dynamic range of 10 3 and a bandwidth of 4–5 GHz, with no ringing seen on the tail of the output pulse. These results represent the best-performing multi-GHz ionizing particle detection system achieved to date.

Ferezghi, Mohammadreza Mohseni [University of Cali↗
DOE OSTI2023

The Energy and Operational Impacts of Using 0-10V Control for LED Streetlights

LED lighting is becoming widely adopted and displacing most traditional lighting technologies. However, the traditional methods used to control light sources have not seen similar displacement. Lighting systems have historically utilized either a proprietary control method, or one of a handful of standardized methods (e.g., 0-10V, DALI, DMX512). The utility and market success of the standardized methods has been limited for a variety of reasons. Analog 0-10V methods have long been popular due to their simplicity and low cost of implementation and are presently the most commonly available control interface for indoor and outdoor LED products in North America. The emergence of “connected lighting systems” with more modern network interfaces and luminaire-level sensors and intelligence was anticipated by many to mark the beginning of the end of analog control. However, 0-10V interfaces continue to be prevalent with these more “digital” systems. The use of 0-10V methods has significant tradeoffs. 0-10V standards have historically not explicitly defined the relationship between the luminaire input control signal and output luminous flux for the full control signal range. As a result, it is difficult to predict relative luminaire light output and input power at any particular control voltage, and in practice the performance across LED drivers and the luminaires power is inconsistent. While this inconsistency has long been acknowledged by experts in the field, it is not accounted for in standard practice deployment, and end-users continue to regularly see unexpected and undesirable performance. It is hoped that the results from this study will help the lighting industry and standards developing organizations better understand and possibly resolve the shortcomings of 0-10V products, and consider what is best for the industry – additional incremental improvements to this fundamentally limited analog technology, or fully moving on to existing digital approaches, such as DALI D4i, that deliver accurate and consistent dimming performance across all luminaires in the system and thus guarantee the delivery of expected light levels, and energy and cost savings. Recommendations consistent with these goals are made to lighting and driver manufacturers, lighting software developers, standard developing organizations, and system designers and specifiers.

0-10V↗
DOE OSTI2023

Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities

Real-time monitoring of combustion behavior is a crucial step toward actively controlled rotating detonation engine (RDE) operation in laboratory and industrial environments. Various machine learning methods have been developed to advance diagnostic efficiencies from conventional postprocessing efforts to real-time methods. Here this work evaluates and compares conventional techniques alongside convolutional neural network (CNN) architectures trained in previous studies, including image classification, object detection, and time series classification, according to metrics affecting diagnostic feasibility, external applicability, and performance. Real-time, capable diagnostics are deployed and evaluated using an altered experimental setup. Image-based CNNs are applied to externally provided images to approximate dataset restrictions. Image classification using high-speed chemiluminescence images and time series classification using high-speed flame ionization and pressure measurements achieve classification speeds enabling real-time diagnostic capabilities, averaging laboratory-deployed diagnostic feedback rates of 4–5 Hz. Object detection achieves the most refined resolution of 20 μs in postprocessing. Image and time series classification require the additional correlation of sensor data, extending their time-step resolutions to 80 ms. Comparisons show that no single diagnostic approach outperforms its competitors across all metrics. This finding justifies the need for a machine learning portfolio containing a host of networks to address specific needs throughout the RDE research community.

33 ADVANCED PROPULSION SYSTEMS↗