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NASA NTRS2015

ORION Environmental Control and Life Support Systems Suit Loop and Pressure Control Analysis

Under NASA's ORION Multi-Purpose Crew Vehicle (MPCV) Environmental Control and Life Support System (ECLSS) Project at Johnson Space Center's (JSC), the Crew and Thermal Systems Division has developed performance models of the air system using Thermal Desktop/FloCAD. The Thermal Desktop model includes an Air Revitalization System (ARS Loop), a Suit Loop, a Cabin Loop, and Pressure Control System (PCS) for supplying make-up gas (N2 and O2) to the Cabin and Suit Loop. The ARS and PCS are designed to maintain air quality at acceptable O2, CO2 and humidity levels as well as internal pressures in the vehicle Cabin and during suited operations. This effort required development of a suite of Thermal Desktop Orion ECLSS models to address the need for various simulation capabilities regarding ECLSS performance. An initial highly detailed model of the ARS Loop was developed in order to simulate rapid pressure transients (water hammer effects) within the ARS Loop caused by events such as cycling of the Pressurized Swing Adsorption (PSA) Beds and required high temporal resolution (small time steps) in the model during simulation. A second ECLSS model was developed to simulate events which occur over longer periods of time (over 30 minutes) where O2, CO2 and humidity levels, as well as internal pressures needed to be monitored in the cabin and for suited operations. Stand-alone models of the PCS and the Negative Pressure relief Valve (NPRV) were developed to study thermal effects within the PCS during emergency scenarios (Cabin Leak) and cabin pressurization during vehicle re-entry into Earth's atmosphere. Results from the Orion ECLSS models were used during Orion Delta-PDR (July, 2014) to address Key Design Requirements (KDR's) for Suit Loop operations for multiple mission scenarios.

Eckhardt, Brad↗

DOE OSTI2022

Legacy surface change analyses of the 1993 Rock Valley earthquake sequence for direct comparison to planned NA-22 underground conventional high-explosive experiments (Source Physics Experiment 3 (SPE3) - RV/DC Task 1.3 FY22 Final Report)

Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.

42 ENGINEERING↗

NASA NTRS2007

Symbolic LTL Compilation for Model Checking: Extended Abstract

In Linear Temporal Logic (LTL) model checking, we check LTL formulas representing desired behaviors against a formal model of the system designed to exhibit these behaviors. To accomplish this task, the LTL formulas must be translated into automata [21]. We focus on LTL compilation by investigating LTL satisfiability checking via a reduction to model checking. Having shown that symbolic LTL compilation algorithms are superior to explicit automata construction algorithms for this task [16], we concentrate here on seeking a better symbolic algorithm.We present experimental data comparing algorithmic variations such as normal forms, encoding methods, and variable ordering and examine their effects on performance metrics including processing time and scalability. Safety critical systems, such as air traffic control, life support systems, hazardous environment controls, and automotive control systems, pervade our daily lives, yet testing and simulation alone cannot adequately verify their reliability [3]. Model checking is a promising approach to formal verification for safety critical systems which involves creating a formal mathematical model of the system and translating desired safety properties into a formal specification for this model. The complement of the specification is then checked against the system model. When the model does not satisfy the specification, model-checking tools accompany this negative answer with a counterexample, which points to an inconsistency between the system and the desired behaviors and aids debugging efforts.

Rozier, Kristin Y.↗

DOE OSTI2021

Targeted Writing and Deleting of Magnetic Skyrmions in Two-Terminal Nanowire Devices

Controllable writing and deleting of nanoscale magnetic skyrmions are key requirements for their use as information carriers for next-generation memory and computing technologies. While several schemes have been proposed, they require complex fabrication techniques or precisely tailored electrical inputs, which limits their long-term scalability. In this work, we demonstrate an alternative approach for writing and deleting skyrmions using conventional electrical pulses within a simple, two-terminal wire geometry. X-ray microscopy experiments and micromagnetic simulations establish the observed skyrmion creation and annihilation as arising from Joule heating and Oersted field effects of the current pulses, respectively. The unique characteristics of these writing and deleting schemes, such as spatial and temporal selectivity, together with the simplicity of the two-terminal device architecture, provide a flexible and scalable route to the viable applications of skyrmions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Ice Supersaturation Variability in Cirrus Clouds: Role of Vertical Wind Speeds and Deposition Coefficients

Abstract Aircraft measurements reveal ice supersaturation statistics in cirrus (ISSs) with broad maxima around ice saturation and pronounced variance. In this study, processes shaping ISSs in midlatitude and tropical upper tropospheric conditions are systematically investigated. Water vapor deposition and sublimation of size‐resolved ice crystal populations are simulated in an air parcel framework. Mesoscale temperature fluctuations (MTFs) due to gravity waves force the temporal evolution of supersaturation. Various levels of background wave forcing and cirrus thickness are distinguished in stochastic ensemble simulations. Kinetic limitations to ice mass growth are brought about by supersaturation‐dependent deposition coefficients that represent efficient and inefficient growth modes as a function of ice crystal size. The simulations identify a wide range of deposition coefficients in cirrus, but most values stay above 0.01 such that kinetic limitations to water uptake remain moderate. Supersaturation quenching times are long, typically 0.5–2 hr. The wave forcing thus causes a remarkably large variability in ISSs and cirrus microphysical properties except in the thickest cirrus, producing ensemble‐mean ISSs in line with in‐situ measurements. ISS variance is controlled by MTFs and increases with decreasing cirrus integral radii. In comparison, the impact of ice crystal growth rates on ISSs is small. These results contribute to efforts directed at identifying and solving issues associated with ice‐supersaturated areas and non‐equilibrium cirrus physics in global models.

Meteorology & Atmospheric Sciences↗

NASA NTRS2002

Identifying head-trunk and lower limb contributions to gaze stabilization during locomotion

The goal of the present study was to determine how the multiple, interdependent full-body sensorimotor subsystems respond to a change in gaze stabilization task constraints during locomotion. Nine subjects performed two gaze stabilization tasks while walking at 6.4 km/hr on a motorized treadmill: 1) focusing on a central point target; 2) reading numeral characters; both presented at 2 m in front at the level of their eyes. While subjects performed the tasks we measured: temporal parameters of gait, full body sagittal plane segmental kinematics of the head, trunk, thigh, tibia and foot, accelerations along the vertical axis at the head and the tibia, and the vertical forces acting on the support surface. We tested the hypothesis that with the increased demands placed on visual acuity during the number recognition task, subjects would modify full-body segmental kinematics in order to reduce perturbations to the head in order to successfully perform the task. We found that while reading numeral characters as compared to the central point target: 1) compensatory head pitch movement was on average 22% greater despite the fact that the trunk pitch and trunk vertical translation movement control were not significantly changed; 2) coordination patterns between head and trunk as reflected by the peak cross correlation between the head pitch and trunk pitch motion as well as the peak cross correlation between the head pitch and vertical trunk translation motion were not significantly changed; 3) knee joint total movement was on average 11% greater during the period from the heel strike event to the peak knee flexion event in stance phase of the gait cycle; 4) peak acceleration measured at the head was significantly reduced by an average of 13% in four of the six subjects. This was so even when the peak acceleration at the tibia and the transmission of the shock wave at heel strike (measured by the peak acceleration ratio of the head/tibia and the time lag between the tibial and head peak accelerations) remained unchanged. Taken together these results provide further evidence that the full body contributes to gaze stabilization during locomotion, and that its different functional elements can be modified online to contribute to gaze stabilization for different visual task constraints.

NASA Center JSC↗

NASA NTRS2010

Improved Hypoxia Modeling for Nutrient Control Decisions in the Gulf of Mexico

As required by the Harmful Algal Bloom and Hypoxia Research Control Act of 1998, the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force issued the 2001 Gulf Hypoxia Action Plan (updated in 2008). In response to the Gulf Hypoxia Action Plan of 2001 (updated in 2008), the EPA Gulf of Mexico Hypoxia Modeling and Monitoring Project has established a detailed model for the Mississippi-Attchafalaya River Basin which provides a capability to forecast the multi-source nutrient loading to the Gulf and the subsequent bio-geochemical processes leading to hypoxic conditions and subsequent effects on Gulf habitats and fisheries. The primary purpose of the EPA model is to characterize the impacts of nutrient management actions, or proposed actions on the spatial and temporal characteristics of the Gulf hypoxic zone. The model is expected to play a significant role in determining best practices and improved strategies for incentivizing nutrient reduction strategies, including installation of on-farm structures to reduce sediment and nutrient runoff, use of cover crops and other agricultural practices, restoration of wetlands and riparian buffers, improved waste water treatment and decreased industrial nitrogen emissions. These decisions are currently made in a fragmented way by federal, state, and local agencies, using a variety of small scale models and limited data. During the past three years, EPA has collected an enormous amount of in-situ data to be used in the model. We believe that the use of NASA satellite data products in the model and for long term validation of the model has the potential to significantly increase the accuracy and therefore the utility of the model for the decision making described above. This proposal addresses the Gulf of Mexico Alliance (GOMA) priority issue of reductions in nutrient inputs to coastal ecosystem. It further directly relates to water quality for healthy beaches and shellfish beds and wetland and coastal conservation restoration.

Habib, Shaid↗

NASA NTRS2003

Identifying Head-Trunk and Lower Limb Contributions to Gaze Stabilization During Locomotion

The goal of the present study was to determine how the multiple, interdependent full-body sensorimotor subsystems respond to a change in gaze stabilization task constraints during locomotion. Nine subjects performed two gaze stabilization tasks while walking at 6.4 km/hr on a motorized treadmill: 1) focusing on a central point target; 2) reading numeral characters; both presented at 2m in front at the level of their eyes. While subjects performed the tasks we measured: temporal parameters of gait, full body sagittal plane segmental kinematics of the head, trunk, thigh, shank and foot, accelerations along the vertical axis at the head and the shank, and the vertical forces acting on the support surface. We tested the hypothesis that with the increased demands placed on visual acuity during the number recognition task, subjects would modify full-body segmental kinematics in order to reduce perturbations to the head in order to successfully perform the task. We found that while reading numeral characters as - compared to the central point target: 1) compensatory head pitch movement was on average 22% greater despite the fact that the trunk pitch and trunk vertical translation movement control were not significantly changed; 2) coordination patterns between head and trunk as reflected by the peak cross correlation between the head pitch and trunk pitch motion as well as the peak cross correlation between the head pitch and vertical trunk translation motion were not significantly changed; 3) knee joint total movement was on average 11% greater during the period from the heel strike event to the peak knee flexion event in stance phase of the gait cycle; 4) peak acceleration measured at the head was significantly reduced by an average of 13% in four of the six subjects. This was so even when the peak acceleration at the shank and the transmissibility of the shock wave at heel strike (measured by the peak acceleration ratio of the head/shank) remained unchanged. Taken together these results provide further evidence that the full body contributes to gaze stabilization during locomotion, and that its different functional elements can be modified online to contribute to gaze stabilization for different visual task constraints.

Mulavara, Ajitkumar P.↗

DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

NASA NTRS2006

Constraining Numerical Geodynamo Modeling with Surface Observations

Numerical dynamo solutions have traditionally been generated entirely by a set of self-consistent differential equations that govern the spatial-temporal variation of the magnetic field, velocity field and other fields related to dynamo processes. In particular, those solutions are obtained with parameters very different from those appropriate for the Earth s core. Geophysical application of the numerical results therefore depends on correct understanding of the differences (errors) between the model outputs and the true states (truth) in the outer core. Part of the truth can be observed at the surface in the form of poloidal magnetic field. To understand these differences, or errors, we generate new initial model state (analysis) by assimilating sequentially the model outputs with the surface geomagnetic observations using an optimal interpolation scheme. The time evolution of the core state is then controlled by our MoSST core dynamics model. The final outputs (forecasts) are then compared with the surface observations as a means to test the success of the assimilation. We use the surface geomagnetic data back to year 1900 for our studies, with 5-year forecast and 20-year analysis periods. We intend to use the result; to understand time variation of the errors with the assimilation sequences, and the impact of the assimilation on other unobservable quantities, such as the toroidal field and the fluid velocity in the core.

Kuang, Weijia↗

DOE OSTI2020

Windowed least-squares model reduction for dynamical systems

Here we propose a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Forest Canopy Density Effects on Snowpack Across the Climate Gradients of the Western United States Mountain Ranges

Limitations inherent to field studies such as limited spatial and temporal coverage has made it very challenging, if not impossible, to explicitly disentangle the effect of canopy density from climate on snowpack dynamics. Hence, our understanding of how variations in canopy density can affect snow processes under diverse climate conditions has been limited. To address this knowledge gap, this study uses a physics-based modeling approach to represent most key processes that drive snow accumulation and melt in the open and forest with varied forest density, across the climate gradients of the Western U.S. mountain ranges, as represented by 228 Snow Telemetry (SNOTEL) locations. Simulations suggest that, under most winter climate conditions, canopy density exerts a strong influence on under-canopy snowpack dynamics and their controlling processes. Changing canopy density can shift the direction of the modeled differential peak snow accumulation and snow disappearance date (SDD) between the open and forest. For example, while simulated snowpack lasts longer in the open than the high-density forest at most wet/warm and dry/cold locations by up to 55 days, about 90% of all locations show a longer snow duration in the low-density forest by up to 27 days as compared to the open. Interannual variability in climate can also change the magnitude and direction of canopy impact on snow processes. By comparing model simulations to published field observations, and we also identify conditions that require additional field observations to improve process-scale understanding of forest-snow interaction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Conserved and reproducible bacterial communities associate with extraradical hyphae of arbuscular mycorrhizal fungi

Abstract Extraradical hyphae (ERH) of arbuscular mycorrhizal fungi (AMF) extend from plant roots into the soil environment and interact with soil microbial communities. Evidence of positive and negative interactions between AMF and soil bacteria point to functionally important ERH-associated communities. To characterize communities associated with ERH and test controls on their establishment and composition, we utilized an in-growth core system containing a live soil–sand mixture that allowed manual extraction of ERH for 16S rRNA gene amplicon profiling. Across experiments and soils, consistent enrichment of members of the Betaproteobacteriales, Myxococcales, Fibrobacterales, Cytophagales, Chloroflexales, and Cellvibrionales was observed on ERH samples, while variation among samples from different soils was observed primarily at lower taxonomic ranks. The ERH-associated community was conserved between two fungal species assayed, Glomus versiforme and Rhizophagus irregularis, though R. irregularis exerted a stronger selection and showed greater enrichment for taxa in the Alphaproteobacteria and Gammaproteobacteria. A distinct community established within 14 days of hyphal access to the soil, while temporal patterns of establishment and turnover varied between taxonomic groups. Identification of a conserved ERH-associated community is consistent with the concept of an AMF microbiome and can aid the characterization of facilitative and antagonistic interactions influencing the plant-fungal symbiosis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Effect of zeolite type, temperature, and pH on Stage III glass alteration behavior for two nuclear waste glasses

We report long-term laboratory scale static alteration tests (up to 1500 d) were conducted on two low-activity waste glasses (LAWA44 and IDF18-A161) at S/V = 2000 m –1 . Tests were seeded with either analcime, clinoptilolite, zeolite P1, or zeolite P2 to induce Stage III glass alteration behavior. The effect of several parameters on glass Stage III behavior was studied with consideration of several variables, including temperature (22 °C – 90 °C) and pH (floating or initially set using KOH at values from 9.5 to 11.5). Temporal solution data and solids characterization at the end of the experiments are reported. Solution data demonstrated the occurrence of a sustained rate acceleration at temperatures down to 40 °C and the activation energy for these Stage III rates was determined for both glasses. Three major secondary phases were identified at the end of the experiments for both glasses: zeolite P1, zeolite P2, and/or analcime (along with minor phases: zeolites, clays, carbonates). The identification of the crystalline phases at the end of the experiment often differed from the zeolite seed phase. In addition, the use of the Avrami equation showed unimpeded zeolite growth (3-dimensional) in certain conditions, suggesting that in certain conditions the glass dissolution rate is controlled by zeolite formation kinetics whereas in other conditions the glass corrosion process impeded zeolite formation kinetics. The morphological evolution of the samples revealed the growth of the zeolites which completely fill the intergranular space in the powder bed. These results are discussed with regards to previous seeded and unseeded Stage III dissolution rate experiments performed on low- and high-activity nuclear waste glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗

DOE OSTI2025

High precision monitoring of outgassing species in model EUV photoresists with a cavity ring-down spectrometer

Extreme ultraviolet (EUV) photoresists play a pivotal role in advancing nanopatterning technologies by balancing image quality and sensitivity. The outgassing behavior of photoresist thin films under EUV and deep ultraviolet (DUV) exposure reveals chemical details relevant to their performance. This study focuses on utilizing an analytical technique not previously used in photolithography, the tabletop cavity ring-down spectrometer, to investigate outgassing dynamics in EUV photoresists, enabling precise chemical identification and deeper insights into resist processing. The spectrometer’s enhanced laser path length (~20 km) and broadband absorption capabilities in the CH overtone region allow for sensitive and temporally resolved detection of outgassed species. Using a model resist comprising a polymer matrix with a photoacid generator and quencher, we analyzed the influence of time delays between exposure and Post-Exposure Bake (PEB) as well as storage under varying environmental conditions. Suppression of isobutylene outgassing and thickness loss was observed with extended delays between exposure and PEB, potentially linked to water absorption and acid deactivation. The technique proved highly effective in distinguishing subtle chemical differences between processing stages. Delay times and their environmental conditions, particularly humidity, reduce outgassing and thickness loss of photoresists during PEB, suggesting decreased acid-driven deprotection. This can potentially impact sensitivity, defectivity, and roughness of resist patterns, necessitating precise monitoring and control.

Lüttgenau, Bernhard↗

NASA NTRSDate not supplied

Isolator Shock Train Response to Dual-Mode Scramjet Throttling-Like Forcing

Typical dual-mode scramjet designs employ an isolator component situated between the inlet and combustor. The isolator fluidically buffers the inlet from the combustor via the formation of a shock train, an interconnected series of shock-boundary layer interactions. The isolator is required during ramjet-engine operation and at the lower edges of the scramjet operational envelope. In an operational engine, transient behavior imposes fluidic dynamics due to attitude adjustments, combustor throttling, aberrant resonances within the combustor, etc. To accommodate these transients, the shock train adjusts on both temporal and spatial scales. Sufficiently large downstream pressure increases in the combustor play a critical role during unstart, whereby strong compression waves propagate upstream in the isolator to ultimately force the shock train out the inlet, if it cannot stabilize to the pressure increase. This critically reduces the engine mass flow rate, potentially causing a catastrophic reduction in thrust and loss of the vehicle. A nearly instantaneous unstart can, in principle, be predicted by a ratio of the normal shock pressure jump in an inviscid isolator. However, lower-magnitude perturbations, below this bound, also induce unstarts. Understanding the transient behavior inherent in unsteady isolator operation is critical to reducing safety margin in isolator length and refining engine control algorithms to improve engine performance. The Isolator Dynamics Research Lab (IDRL) is a cold-flow, direct-connect isolator facility ideal for assessing the fundamental fluid dynamics of shock trains. The internal flow of a scramjet isolator is modeled using a 2D, symmetric Mach 2.5 nozzle followed by a 695 mm long, 25.4x50.8 mm rectangular isolator test section. A blockage cone on a high-speed ball screw actuator downstream of the isolator is pulsed to simulate the throttling behavior in an operational combustor. Wall flush high-speed static pressure transducers detect the streamwise shock train within the isolator. Time delays between the onset of the throttling and the response motion of the shock train are analyzed to assess the controlling scales and parameters. From this analysis, the response time of the isolator shock train system is found to depend primarily on the isolator back pressure time-rate-of-change but quickly saturates to a maximum propagation speed along the isolator. These results inform dual-mode scramjet engine designers of the critical parameters and limits of an unstart resistant engine.

scramjet↗