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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

From MAST-U to STEP: Power exhaust control challenges and opportunities

This work explores the challenges and opportunities for power exhaust control in STEP, informed by dedicated MAST-U experiments. The STEP system primarily relies on hydrogenic and argon gas injection into the divertors, compensating for transients originating in the plasma core. Fast transients (>10 Hz) that evolve too quickly for actuators to compensate must be absorbed passively; the enhanced transient buffering of the foreseen long-legged divertor provides a clear advantage over conventional geometries, as demonstrated in the MAST-U Super-X divertor. Experiments further indicate that STEP’s tight divertor baffling enables near-independent control of the upper and lower divertors. The most challenging transients are expected from power-sharing fluctuations, as MAST-U experiments observed extremely fast dynamics. Fluctuations from core pellet fuelling remain relatively benign due to the small pellet size according to simulations. Exhaust control in a reactor like STEP requires an integrated approach befitting its machine-critical nature. A predictive control element, integrated with core plasma control, allows pre-emptive preparation of the divertor for incoming transients. An observer-based diagnostic approach is foreseen to monitor the divertor in the challenging reactor environment, supported by an extended diagnostic set in the non-nuclear phase to validate the required dynamic models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Optical Spectrometer With a Pulse-to-Pulse Resolution for Terahertz and mm-Wave Signals

The development of ultra-compact particle accelerators and accelerator-based high-power THz generators are some of the top priorities for research and development programs around the world. These tools require the development of accelerating structures, operating in the mm-Wave and THz range, where diagnostics are not available or are insufficient. This article presents the design of a pulse-to-pulse THz spectrometer, primarily developed for diagnostics of accelerating structures and THz radiation sources. In particular, this spectrometer can be used to detect RF pulse shortening caused by vacuum breakdown and beam misalignment in THz accelerators, as well as for bunch length monitoring and radiation source diagnostics. The spectrometer is based on a diffraction grating and is capable of covering a frequency range where RF-based breakdown measurements are not possible. We have built and tested the first prototype with different sources in a frequency range of 0.1-1.0 THz. In this article, we present the physical design, supported numerical simulations, electronics development, and test results.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Dielectrophoresis assisted rapid, selective and single cell detection of antibiotic resistant bacteria with G-FETs

Time-consuming, expensive and low sensitivity diagnostic methods used for monitoring bacterial infections lead to unnecessary or delays in prescription of the right antibiotic treatment. Determining an optimal clinical treatment requires rapid detection and identification of pathogenic bacteria and their sensitivity to specific antimicrobials. However, diagnostic devices that meet all of these criteria have proven elusive thus far. Graphene field effect transistors (G-FET) are a promising solution, since they are highly sensitive to chemical/biological modification, can have fast detection times and can be placed on different substrates. Here, by integrating specific peptide probes over G-FETs, we present a proof-of-concept study for species and strain specific label-free detection of clinical strains of pathogenic bacteria with high specificity and sensitivity. We found that pyrene-conjugated peptides immobilized on G-FETs were capable of detecting pathogenic Staphylococcus aureus at the single-cell level and discriminate against other gram-positive and gram-negative bacterial pathogens. A similar device was able to discriminate between antibiotic resistant and sensitive strains of Acinetobacter baumannii , suggesting that these devices can also be used for detecting antibiotic resistive pathogens. Furthermore, a new means of enhancing attachment, electric-field assisted binding, reduced the detection limit to 104 cells/ml and the detection time to below 5 minutes. Here, the combination of single step attachment, inexpensive production, rapid, selective and sensitive detection suggest G-FETs plus pyrene-conjugated peptides are a new platform for solving major challenges faced in point of care diagnostics to fight infectious diseases and antimicrobial resistance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2015

Testing of the J-2X Augmented Spark Igniter (ASI) and Its Electronics

Reliable operation of the spark ignition system electronics in the J‐2X Augmented Spark Igniter (ASI) is imperative in assuring ASI ignition and subsequent Main Combustion Chamber (MCC) ignition events are reliable in the J‐2X Engine. Similar to the man‐rated J‐2 and RS‐25 engines, the J‐2X ignition system electronics are equipped with spark monitor outputs intended to indicate that the spark igniters are properly energized and sparking. To better understand anomalous spark monitor data collected on the J‐2X development engines at NASA Stennis Space Center (SSC), a comprehensive subsystem study of the engine's low‐ and high‐tension spark ignition system electronics was conducted at NASA Marshall Space Flight Center (MSFC). Spark monitor output data were compared to more detailed spark diagnostics to determine if the spark monitor was an accurate indication of actual sparking events. In addition, ignition system electronics data were closely scrutinized for any indication of an electrical discharge in some location other than the firing tip of the spark igniter ‐ a problem not uncommon in the development of high voltage ignition systems.

Osborne, Robin↗
DOE OSTI2026

Measurement of the Energy Spread for CeC Project

The Coherent Electron Cooling requires small energy spread and uniformity of energy along the bunch. The diagnostics line is utilized for the measurement of the electron beam parameters. The beamline layout is shown in Fig. 1. Three quadrupoles after the linac are used to match beam into the common section. The main dipole is used to deflect beam towards common section. If it is switched off the beam goes to the diagnostics line. The beam optics in the diagnostics line is controlled by four quadrupoles. The deflecting cavity sweeping beam in the vertical direction follows the quadrupoles. Sector dipole is used for energy parameters measurement. It has deflection angle of 30 degrees. With diagnostics dipole switched off the beam propagated to the insertable slits system for emittance measurement. There are four profile monitors in the line, The first profile monitor (ACC YAG) is after the main dipole. The second profile monitor (YAG3) is in front of the diagnostics dipole after the deflecting cavity. YAG1 profile monitor is placed after the slits, and YAG2 profile monitor is placed after the diagnostics dipole. There is a solenoid between the deflecting cavity and YAG3. It is used for the beam energy measurement.

43 PARTICLE ACCELERATORS↗
NASA NTRS2001

Health Monitoring Technology for Thermal Protection Systems on Reusable Hypersonic Vehicles

Integrated subsystem health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles (RLVs) in order to reduce life cycle costs, to increase safety margins, and to improve mission reliability. This talk summarizes a joint effort between NASA Ames and industry partners to develop rapid non-contact diagnostic tools for health and performance monitoring of thermal protection systems (TPS) on future RLVs. The specific goals for TPS health monitoring are to increase the speed and reliability of TPS inspections for improved operability at lower cost. The technology being developed includes a 3-D laser scanner for examining the exterior surface of the TPS, and a subsurface microsensor suite for monitoring the health and performance of the TPS. The sensor suite consists of passive overlimit sensors and sensors for continuous parameter monitoring in flight. The sensors are integrated with radio-frequency identification (RFID) microchips to enable wireless communication of-the sensor data to an external reader that may be a hand-held scanner or a large portal. Prototypes of the laser system and both types of subsurface sensors have been developed. The laser scanner was tested on Shuttle Orbiter Columbia and was able to dimension surface chips and holes on a variety of TPS materials. The temperature-overlimit microsensor has a diameter under 0.05 inch (suitable for placement in gaps between ceramic TPS tiles) and can withstand 700 F for 15 minutes.

Milos, Frank S.↗
DOE OSTI2025

Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
DOE OSTI2022

LCOE reduction through proactively optimized monitoring of PV Systems (Final Technical Report)

The project demonstrates the value proposition for a high-resolution monitoring system (HRMS) with diagnostic-prognostic capability and determine its impact on LCOE. The HRMS differs from conventional monitoring systems in a number of ways. First it will include the capability to automatically measure IV curves at the string and module levels. This provides a much richer view into the DC performance of the PV system and allows classification of many typical failure and degradation modes. Second, it will incorporate software capable of quantifying power and energy losses in the field as well as define the location and mechanism of the power and energy loss. Moreover, we aimed to deliver a prognostic system that is capable of predicting certain failures before they occur and giving system operators the opportunity to more efficiently plan operations and maintenance (O&M) activity in order to lower costs and increase yield over the life of the system. The research identifies cost targets required for different monitoring stages, including at the string combiner, at the individual string, and at the module level, to lower the levelized cost of energy (LCOE). Finally, a comprehensive guide determines the value PV monitoring brings to PV field operations. The guide assists plant operators in maximizing value from existing plants and identify the trade-offs of different monitoring solutions for future plants depending on the size, location, and expected system lifetime.

14 SOLAR ENERGY↗
NASA NTRS2004

Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates whose consistency can be tracked across multiple time steps.

Narasimhan, Sriram↗
DOE OSTI2025

Optical Test Bench (SYSM 5620 Final Report)

The National Ignition Facility (NIF) is a research facility that focuses on High Energy Density Science and Inertial Confinement Fusion. In the NIF, 192 laser beams are directed into a miniscule Target Capsule to generate an implosion – this is called a NIF Shot. Many NIF Users conduct experiments using different Targets and laser inputs to generate reactions that are of interest to the scientific community. Those reactions are monitored using various Target Diagnostic instruments, including Shot Time Photography – which provides crucial images of the Target as the implosion occurs. A subset of these NIF Users is less interested in the reaction itself and more interested in subjecting materials or devices to the intense environment that these reactions can create. Using the Target as a source of x-rays, neutrons, or blast loading for particular materials has been significant to the shock physics community and to those who work in extreme design spaces. However, the Material and Radiation Effects team – that performs these experiments on behalf of the NIF Users – has not been able to make good use of the existing Shot Time Photography Diagnostics. The STP cameras are focused on the Targets rather than the MRE Customers’ materials, the current system is not oriented to have a good view of the MRE Customers’ materials, and the cameras are limited in their capabilities (black and white, poor resolution, etc.). As MRE experiments increase in quantity and importance to the scientific community, the desire for visual monitoring of the experiments to substantiate models and inform other data is also rising.

42 ENGINEERING↗
DOE OSTI2021

Energy Management Information System Powers NREL's Intelligent Campus

NREL's Intelligent Campus program leverages its own laboratory buildings as research instruments to study renewable energy, energy efficiency, and energy storage, integration, and analysis with real, quantitative measurements. At the heart of NREL's Intelligent Campus program is its Energy Management Information System (EMIS), a family of tools and services used to manage building and campus energy use. NREL's EMIS includes capabilities, such as benchmarking and monthly utility tracking, interval meter analytics, equipment fault detection and diagnostics, condition-based monitoring, and supervisory control, enabling unprecedented energy management capabilities. The system serves as a demonstration project for other federal facilities interested in learning about its design, features, and benefits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A digital holography ex situ measurement characterization of plasma-exposed surface erosion from an electrothermal arc source

Digital holography has been proposed to fulfill a need for an imaging diagnostic capable of in situ monitoring of surface erosion caused by plasma–material interaction in nuclear fusion devices. A digital holography diagnostic for 3D surface erosion measurement has been developed at Oak Ridge National Laboratory with the goal of deployment on a plasma device. A proof-of-concept in situ demonstration is planned which would involve measurement of plasma erosion on targets exposed to an electrothermal arc source. This work presents the results of an ex situ characterization of the capability and limitations of holographic imaging of targets exposed to the arc source. Targets were designed to provide a fiducial for comparison of deformed and unaffected areas. The results indicated that the average net erosion was ~150 nm/plasma exposure, which is expected to be within the diagnostic’s measurement capacity. Surface roughness averages determined by holographic image analysis showed good agreement with measurements taken with a profilometer. Finally, the limit of the holography diagnostic’s x–y spatial resolution was characterized by comparison with scanning electron microscope imaging.

47 OTHER INSTRUMENTATION↗
NASA NTRS2002

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Kobayashi, Takahisa↗
DOE OSTI2022

A laser powder bed fusion system for operando synchrotron x-ray imaging and correlative diagnostic experiments at the Stanford Synchrotron Radiation Lightsource

Laser powder bed fusion (LPBF) is a highly dynamic multi-physics process used for the additive manufacturing (AM) of metal components. Improving process understanding and validating predictive computational models require high-fidelity diagnostics capable of capturing data in challenging environments. Synchrotron x-ray techniques play a vital role in the validation process as they are the only in situ diagnostic capable of imaging sub-surface melt pool dynamics and microstructure evolution during LPBF-AM. In this article, a laboratory scale system designed to mimic LPBF process conditions while operating at a synchrotron facility is described. The system is implemented with process accurate atmospheric conditions, including an air knife for active vapor plume removal. Significantly, the chamber also incorporates a diagnostic sensor suite that monitors emitted optical, acoustic, and electronic signals during laser processing with coincident x-ray imaging. The addition of the sensor suite enables validation of these industrially compatible single point sensors by detecting pore formation and spatter events and directly correlating the events with changes in the detected signal. Experiments in the Ti–6Al–4V alloy performed at the Stanford Synchrotron Radiation Lightsource using the system are detailed with sufficient sampling rates to probe melt pool dynamics. X-ray imaging captures melt pool dynamics at frame rates of 20 kHz with a 2 µm pixel resolution, and the coincident diagnostic sensor data are recorded at 470 kHz. Furthermore, this work shows that the current system enables the in situ detection of defects during the LPBF process and permits direct correlation of diagnostic signatures at the exact time of defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
DOE OSTI2020

Laser speckle contrast imaging of blood microcirculation in pancreatic tissues during laparoscopic interventions

Laser speckle contrast imaging of the microcirculatory bed of the pancreas is performed, which allows its condition to be assessed and thereby is an additional valuable tool for making a diagnostic decision and dynamically monitoring the effectiveness of the treatment for pathology of the abdominal organs. Laparoscopic operations on the pancreas are low-traumatic and in most cases avoid open surgical interventions. For the first time an experimental system for recording speckle images, combined with a commercially available five-millimetre rigid laparoscope, is presented. The sensitivity of the system to the fluid motion in a capillary at different velocities is determined, and the possibility of finding areas of blood microcirculation disturbance in modelling pancreatic ischemia in an experiment on laboratory animals is revealed. The laparoscope illumination channel is verified by comparison with speckle dynamics under external illumination of the studied object. (paper)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Industry 4.0 and Intelligent Manufacturing Processes: A Review of Modern Sensing Technologies

Digital Manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create a more lean, profitable, and data-driven manufacturing processes. In this paper, we review modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to Cyber-Physical Systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations.

42 ENGINEERING↗