Mitigation Strategies for Diagnostic Data Degradation due to High Neutron Yield of ICF Experiments at the NIF
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Methanol is considered a promising alternative fuel for internal combustion engines (ICEs) due to its high-octane number, fast laminar flame speed, and elevated latent heat of vaporization, all of which support higher compression ratios and improved thermal efficiency. However, its substantial latent heat of vaporization also poses cold-start challenges, such as misfire and fuel film deposition. This study aims to investigate methanol spray morphology and spray-wall interaction using the Spray M injector from the Engine Combustion Network within a constant-pressure flow vessel. A recently developed unified numerical framework capable of modeling both flash and non-flash boiling sprays is validated against experimental liquid volume fraction data acquired via 3-D computed tomography. Here, the results reveal that flash boiling significantly alters the spray morphology, leading to smaller droplets and spray collapse due to enhanced air-entrainment-induced turbulence. Quantitative agreement between experiments and simulations confirms this behavior. Coupled 0-D equilibrium and 3-D computational fluid dynamics analyses show that flash boiling accelerates evaporation and reduces fuel residence time, while non-flash conditions maintain a persistent liquid core more susceptible to wall wetting. Wall temperature diagnostics reveal that spray collapse alters heat transfer patterns by shifting cooling effects. Mixture fraction analysis indicates that evaporation is primarily governed by shear-layer turbulence, though deviations from adiabatic equilibrium mixing emerge under low-turbulence conditions. Finally, increasing fuel, ambient, and wall temperatures reduces wall wetting and film thickness, mitigating cold-start risks. These findings enhance the understanding of methanol sprays’ behavior and support its adoption as a viable, alternative fuel for ICEs.
We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.
The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.
The facility target fabrication and flow testing activity will consist of flow tests using the blower system at LANL to test the disk holder and target, both redesigned by the NorthStar prime contractor. The housing and transition system delivering helium cooling to the disc holder and target will be fabricated and tested at LANL facilities. The tests will include pressure drop versus flow measurements through the target and housing, optical flow diagnostics and fiber optic imaging of the disc stack. The pressure drop measurements will be compared to CFD simulations to verify the latter, the optical flow diagnostics will be used to determine whether modifications made to the housing and target holder allow sufficient flow through the bottom of the housing to adequately cool it and the fiber optics will be used to image the discs during operation as a proof of concept of the diagnostic, to be used at NorthStar facilities during preproduction testing. The work is a Partial Fulfillment of the Deliverable Requirement for NorthStar Facility Target Fab and Flow Testing and Production Target Testing Support by LANL
The U.S. Department of Energy, National Nuclear Security Administration, Los Alamos Field Office (NA-LA), documented and evaluated seven structures in the Technical Area 60 (TA-60) Rack Assembly and Alignment Complex (RAAC) legacy storage yard at Los Alamos National Laboratory (LANL or Laboratory) for listing in the National Register of Historic Places (National Register). This documentation and evaluation was conducted in compliance with Section 106 of the National Historic Preservation Act of 1966, as amended; the Code of Federal Regulations (36 CFR 800); the Programmatic Agreement among the U.S. Department of Energy, National Nuclear Security Administration, Los Alamos Field Office, the New Mexico State Historic Preservation Office and the Advisory Council on Historic Preservation Concerning Management of the Historic Properties at Los Alamos National Laboratory, Los Alamos New Mexico (PA)1; and A Plan for the Management of the Cultural Heritage at Los Alamos National Laboratory, New Mexico (LANL’s Cultural Resources Management Plan (2017). NA-LA makes the following National Register eligibility determinations: la cuna; the rack transporter (jeep, rack trailer, and steering dolly); the Mexia diagnostic rack; the Mexia device canister; the Mexia device mounting stand; and the Mexia target stand are eligible for listing in the National Register, and the second steering dolly is not eligible for listing in the National Register. This National Register evaluation was completed because LANL proposes to develop a consolidated waste facility in TA-60 next to the former RAAC. Across 3.56 acres, LANL proposes to construct an 8,000-square-foot, pre-engineered waste storage building; a 1,500-square-foot office/warehouse building; and 28,500 square feet of covered storage. The development will also include access control features and fencing, parking spaces, and utilities. Additionally, the Laboratory will continue to use the southern part of TA-60-0017 and reuse TA-60-0086 and TA-60-0324 in the development. The consolidated waste facility will operate as a central accumulation area—storing universal waste, mixed low-level (radioactive) waste, hazardous chemicals, and New Mexico special waste—until the waste can be shipped off site.
In the present work, a first-of-its-kind three-dimensional (3D) large-eddy simulation (LES) study is conducted to numerically investigate the combustion dynamics as well as aero-thermal phenomena in a full-scale nonpremixed hydrogen–air rotating detonation engine (RDE) (with a diverging-shaped lower-end wall), when integrated with nozzle guide vanes (NGV) acting as the turbine stator. The wall-modeled LES framework incorporates hydrogen–air detailed chemical kinetics and adaptive mesh refinement (AMR). A comparative analysis is carried out for two operating conditions with different fuel/air mass flow rates but global equivalence ratio of unity, and considering RDE configurations without and with stator. The LES model is validated against available experimental data for the low mass flux condition with respect to detonation wave speed/height, wave dynamics, and axial static pressure distribution. Numerical results indicate significant deflagrative combustion occurring in the fill region near the inner wall due to formation of recirculation zones in the injection near-field driven by the backward facing step. The leading detonation wave is found to be trailed by an azimuthal reflected-shock combustion (ARSC) wave, consistent with experimental observations, which consumes unburned vitiated reactants that leak through the main detonation wave. The main detonation wave characteristics, such as detonation wave speed/height and combustion efficiency, do not change appreciably with the presence of NGV. A novel combustion diagnostic technique based on chemical explosive mode analysis (CEMA) is employed to quantify the fraction of heat release occurring in the detonative mode versus deflagrative mode for the simulated conditions. The exit flow is found to be nearly fully subsonic and supersonic for the low and high mass flux conditions, respectively. Further analysis of the exit flow profiles shows that the presence of NGV renders the flow more axial and significantly impacts the exit Mach number and total pressure, while the total temperature shows negligible change. In addition, the low mass flux operating point, despite exhibiting more deflagrative losses within the combustor, yields overall lower pressure drop from plenum to exhaust, which is mainly attributed to lower pressure drop across the injectors. Lastly, the rotating detonation engine-nozzle guide vanes (RDE-NGV) configuration exhibits higher total pressure loss compared to rotating detonation engine (RDE) without stator across both the mass flux conditions. In conclusion, this study extends the state-of-the-art in numerical modeling of pressure gain combustion (PGC) systems by demonstrating high-fidelity 3D reacting LES of full-scale RDE-NGV systems relevant to RDE-turbine integration for stationary power generation.
The electrochemical performance and cycle life of lithium-ion batteries (LIBs) depend on the electrochemical, chemical, and mechanical behavior of electrodes and electrolytes. Despite extensive studies conducted previously, challenges exist to decouple these behaviors, capture the evolution of electro-chemo-mechanical behavior in realistic conditions, and correlate atomic-scale stress evolution to micro-scale bulk mechanical degradation. Here, we report multiscale operando techniques to investigate polydisperse battery electrodes by integrating volume-averaged quantitative synchrotron X-ray scattering with high-resolution transmission X-ray microscopy (TXM). The former provides us information spanning a wide spatial range, from Angstrom-level atomic structures to micrometer-level particle scales, while the latter provides time-resolved 2D images of the particles during cycling. The complementarity of the two operando techniques is demonstrated by an over-lithiation test of LiCoO 2 electrodes, where particles crack and eventually pulverize. Additionally, the techniques are applied to study LiCoO 2 cycling stability from 3.0 V to 4.5 V. Operando X-ray scattering result shows nanometer-scale features keep forming in LiCoO 2 electrodes during cycling, resulting in an increased projected area observed by the TXM experiment. The formation of such features is inhibited by a polymer coating on the electrode, leading to vastly improved cycling stability. The polymer coating alleviates LiCoO 2 surface deterioration, reduces side product generation, and inhibits LiCoO 2 particles volume expansion during the cycling test. These operando multimodal X-ray techniques presented herein thus offer a novel, multiscale diagnostic modality for studying existing and emerging battery materials, aiding the development of next-generation LIBs.
Testing of a compact Bremsstrahlung diode at the High Energy Radiation Megavolt Electron Source III (HERMES-III) was performed at Sandia National Laboratories in November, 2023. The compact diode described here is the first prototype diode in a campaign to optimize a Bremsstrahlung diode in terms of size and dose production. The goal was to test the diode at 13MV, and the experiment realized between 10-12MV at the diode. Modeling and simulation of this geometry was performed after the test, shedding insight into several phenomena seen by experimental diagnostics. Modifications to the diode and experiment are proposed for future experiments to help explain the phenomena and approach a better final design.
This presentation is a technical summary of and outlook for NASA-internal and NASA-sponsored external research on core (combustor and turbine) noise funded by the Fundamental Aeronautics Program Subsonic Fixed Wing (SFW) Project. Sections of the presentation cover: the SFW system-level noise metrics for the 2015, 2020, and 2025 timeframes; turbofan design trends and their aeroacoustic implications; the emerging importance of core noise and its relevance to the SFW Reduced-Perceived-Noise Technical Challenge; and the current research activities in the core-noise area, with additional details given about the development of a high-fidelity combustor-noise prediction capability as well as activities supporting the development of improved reduced-order, physics-based models for combustor-noise prediction. The need for benchmark data for validation of high-fidelity and modeling work and the value of a potential future diagnostic facility for testing of core-noise-reduction concepts are indicated. The NASA Fundamental Aeronautics Program has the principal objective of overcoming today's national challenges in air transportation. The SFW Reduced-Perceived-Noise Technical Challenge aims to develop concepts and technologies to dramatically reduce the perceived aircraft noise outside of airport boundaries. This reduction of aircraft noise is critical to enabling the anticipated large increase in future air traffic. Noise generated in the jet engine core, by sources such as the compressor, combustor, and turbine, can be a significant contribution to the overall noise signature at low-power conditions, typical of approach flight. At high engine power during takeoff, jet and fan noise have traditionally dominated over core noise. However, current design trends and expected technological advances in engine-cycle design as well as noise-reduction methods are likely to reduce non-core noise even at engine-power points higher than approach. In addition, future low-emission combustor designs could increase the combustion-noise component. The trend towards high-power-density cores also means that the noise generated in the low-pressure turbine will likely increase. Consequently, the combined result from these emerging changes will be to elevate the overall importance of turbomachinery core noise, which will need to be addressed in order to meet future noise goals.
We have synthesized Fe–Ru nanoparticles via a solvothermal method to create catalysts for the reverse water–gas shift reaction and demonstrated the impact of reductive and oxidative pretreatments on both catalytic performance and structure. Catalytic testing showed improved activity after exposure to O 2 at 600 °C. In contrast, the activity became lower if then exposed to H 2 at 600 °C. Environmental scanning transmission electron microscopy and scanning electron microscopy showed that exposure to O 2 at 600 °C changes the morphology and completely oxidizes Fe into Fe 2 O 3 . Exposure to H 2 at elevated temperature caused Ru coalescence at the surface of the nanoparticle, forming clusters which decreased the optimization of Ru. Reoxidation of the particles exposed to H 2 , however, caused a redistribution of Ru that appears beneficial in maximizing Ru exposure and synergy with Fe oxide, with no major changes in the morphology and oxide structure. Furthermore, our diagnostics demonstrate the complex and reversible rearrangements possible in these multicomponent particles and the benefits of oxidative pretreatment to enhance or regenerate Fe–Ru catalysts in other important catalytic reactions such as Fischer–Tropsch synthesis.
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.
An advanced 22-in. scale model turbofan, typical of a current-generation aircraft engine design by GE Aircraft Engines, was tested in NASA Glenn Research Center s 9- by 15- Foot Low-Speed Wind Tunnel to explore the far-field acoustic effects of an increased bypass nozzle area at simulated aircraft speeds of takeoff, approach, and landing. The wind-tunnel-scale model consisted of the bypass stage fan, stators, and nacelle (including the fan exit nozzle) of a typical turbofan. This fan-stage test was part of the NASA Glenn Fan Broadband Source Diagnostic Test, second entry, which acquired aeroacoustic results over a range of test conditions. A baseline nozzle was selected, and the nozzle area was chosen for maximum performance at sea-level conditions. Two additional nozzles were also tested--one with a 5.4-percent increase in nozzle area over the baseline nozzle (sized for design point conditions), corresponding to a 5-percent increase in fan weight flow, and another nozzle with a 10.9-percent increase in nozzle area over the baseline nozzle (sized for maximum weight flow at sea-level conditions), corresponding to a 7.5 percent increase in fan weight flow. Measured acoustic benefits with increased nozzle area were very encouraging, showing overall sound power level reductions of 2 dB or more (left graph) while the stage adiabatic efficiency (right graph) and thrust (final graph) actually increased by several percentage points. These noise-reduction benefits were seen to include both rotor-interaction tones and broadband noise, and were evident throughout the range of measured sideline angles.
Wave motion during capsule recovery operations can result in motion sickness and performance decrements exacerbating reentry sickness following long duration spaceflight. The purpose of this pilot study was (1) to validate a capsule wave motion simulation as a platform to evaluate motion sickness countermeasures and (2) to evaluate a sensory augmentation belt providing vibrotactile feedback of gravitational upright. Ten healthy subjects ages 38.0 ± 10.1 (6M|4F) were exposed to complex wave motions on a six degree-of-freedom platform that included pitch, roll, and heave at provocative stimulus frequencies (0.1-0.25 Hz) while seated in an illuminated cabin deprived of external visual cues. Subjects reported acute symptoms for up to three consecutive 15 min trials or until they reached a motion sickness endpoint of 8 pts on the Pensacola Diagnostic Index (PDI). Five subjects were randomly assigned to the Sensory Augmentation (SA) group while the other five served as controls (CN group). Based on a Motion Sickness Susceptibility Questionnaire, the two groups had similar motion sickness histories (susceptibility percentile ranking of CN group = 27.5 ± 63.6 in the CN group versus 27.5 ± 37.0 in the SA group, median ± IQR). The vibrotactile feedback consisted of a single array of 8 electromechanical tactors positioned around the torso on an adjustable belt (Engineering Acoustics, Inc) that utilized an integrated inertial measurement unit (IMU) to indicate the direction of upright (e.g., subject’s back tactor on during forward tilt). During each wave motion trial subjects performed a battery of four different tasks: tracking Earth vertical using a joystick with and without a secondary task (Paced Auditory Serial Addition Test), an eye-hand target acquisition task on a cabin-fixed tablet, and the psychomotor vigilance test (PVT). All ten subjects reported varying levels of motion sickness with 6 of 10 reaching a symptom endpoint. Interestingly, subjects anecdotally reported that engagement in the joystick tracking task was less provocative than tasks involving the cabin-fixed tablet or periods of no activity. Sensory augmentation appeared to delay symptom onset, with 2 of 5 subjects reaching an endpoint within the first 15 min trial in the CN group versus none in the SA group (PDI after 15 min = 6.0 ± 2.5 in the CN group versus 3.4 ± 2.5 in the SA group, mean ±std). Sensory augmentation also improved performance on the joystick tracking task at lower stimulus frequencies (0.1 Hz in roll and 0.2 Hz in pitch). Both CN and SA groups maintained a consistent level of performance on the eye-hand target acquisition and PVT throughout the baseline (no motion) and wave motion periods. Our results validated that the simulated capsule wave motion paradigm provides an effective motion sickness stressor. This paradigm is currently being used to investigate the efficacy of intranasal scopolamine to mitigate motion sickness. Sensory augmentation using vibrotactile feedback appears to improve spatial awareness and delay symptom onset during complex passive motion. One advantage of this portable belt design is that it incorporates all tactor drive and IMU circuitry and therefore could continue to be worn by crewmembers and serve as a balance aid during egress and ambulation with recovery operations.
Adenosine-5’-triphosphate (ATP), the primary energy currency in cellular processes, drives metabolic activities and biosynthesis. Despite its importance, understanding intracellular ATP dynamics’ impact on bioproduction and exploiting it for enhanced bioproduction remains largely unexplored. Here, we harness an ATP biosensor to dissect ATP dynamics across different growth phases and carbon sources in multiple microbial strains. We find transient ATP accumulations during the transition from exponential to stationary growth phases in various conditions, coinciding with fatty acid (FA) and polyhydroxyalkanoate (PHA) production in Escherichia coli and Pseudomonas putida, respectively. We identify carbon sources (acetate for E. coli, oleate for P. putida) that elevate steady-state ATP levels and boost FA and PHA production. Moreover, we employ ATP dynamics as a diagnostic tool to assess metabolic burden, revealing bottlenecks that limit limonene bioproduction. Our results not only elucidate the relationship between ATP dynamics and bioproduction but also showcase its value in enhancing bioproduction in various microbial species.
This paper presents the design for a switching magnet capable of deflecting 6.5 MeV/u heavy-ion beams with mass-to-charge ratio of 7 by 10 degrees. This magnet can produce variable flat-top pulses from 1 to 30 ms with rise and fall times of less than 0.5 ms at a variable duty cycle of 3–91% into a heavily inductive load. The magnet has a laminated, iron dominated, H-shaped core. Directly-cooled copper pancake coils energize the magnet. We present the magnet design considerations, including the results of extensive computer modeling. Finally, we also present the conceptual engineering design and the studies of magnet yoke fabrication.
Gas gun shock-to-detonation experiments provide data for calibrating HE burn models. At LANL the principal diagnostic is a magnetic gauge package that provides data on the lead shock trajectory, which is used to determine the run-distance to detonation, and the lead shock speed with run distance, which together with jump-off velocity of the velocity gauges determines Hugoniot locus points for the reactants.
This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.