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NASA NTRS1990

Cloud parameters from GOES visible and infrared radiances during the FIRE Cirrus IFO, October 1986

Visible (VIS, 0.65 micron) and infrared (IR, 10.5 microns) channels on geostationary satellites are the key elements of the International Satellite Cloud Climatology Project (ISCCP). All daytime ISCCP cloud parameters are derived from a combination of VIS and IR data. Validation and improvement of the ISCCP and other cloud retrieval algorithms are important components of the First ISCCP Regional Experiment (FIRE) Intensive Field Observations (IFO). Data from the Cirrus IFO (October 19 to November 2, 1986) over Wisconsin are available for validating cirrus cloud retrievals from satellites. The Geostationary Operational Environmental Satellite (GOES) located over the Equator at approximately 100 deg W provided nearly continuous measurements of VIS and IR radiances over the IFO areas. The preliminary results of cloud parameters derived from the IFO GOES data are presented. Cloud attitudes are first derived using an algorithms without corrections for cloud emissivity. These same parameters will then be computed from the same data relying on an emissivity correction algorithm based on correlative data taken during the Cirrus IFO.

Heck, Patrick W.↗

NASA NTRS1994

Automated inspection of turbine blades: Challenges and opportunities

Current inspection methods for complex shapes and contours exemplified by aircraft engine turbine blades are expensive, time-consuming and labor intensive. The logistics support of new manufacturing paradigms such as integrated product-process development (IPPD) for current and future engine technology development necessitates high speed, automated inspection of forged and cast jet engine blades, combined with a capability of retaining and retrieving metrology data for process improvements upstream (designer-level) and downstream (end-user facilities) at commercial and military installations. The paper presents the opportunities emerging from a feasibility study conducted using 3-D holographic laser radar in blade inspection. Requisite developments in computing technologies for systems integration of blade inspection in production are also discussed.

Mehta, Manish↗

NASA NTRS2002

Memory-Intensive Benchmarks: IRAM vs. Cache-Based Machines

The increasing gap between processor and memory performance has lead to new architectural models for memory-intensive applications. In this paper, we explore the performance of a set of memory-intensive benchmarks and use them to compare the performance of conventional cache-based microprocessors to a mixed logic and DRAM processor called VIRAM. The benchmarks are based on problem statements, rather than specific implementations, and in each case we explore the fundamental hardware requirements of the problem, as well as alternative algorithms and data structures that can help expose fine-grained parallelism or simplify memory access patterns. The benchmarks are characterized by their memory access patterns, their basic control structures, and the ratio of computation to memory operation.

Biswas, Rupak↗

NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗

NASA NTRS1992

Colour thresholding and objective quantification in bioimaging

Computer imaging is rapidly becoming an indispensable tool for the quantification of variables in research and medicine. Whilst its use in medicine has largely been limited to qualitative observations, imaging in applied basic sciences, medical research and biotechnology demands objective quantification of the variables in question. In black and white densitometry (0-256 levels of intensity) the separation of subtle differences between closely related hues from stains is sometimes very difficult. True-colour and real-time video microscopy analysis offer choices not previously available with monochrome systems. In this paper we demonstrate the usefulness of colour thresholding, which has so far proven indispensable for proper objective quantification of the products of histochemical reactions and/or subtle differences in tissue and cells. In addition, we provide interested, but untrained readers with basic information that may assist decisions regarding the most suitable set-up for a project under consideration. Data from projects in progress at Tulane are shown to illustrate the advantage of colour thresholding over monochrome densitometry and for objective quantification of subtle colour differences between experimental and control samples.

NASA Discipline Neuroscience↗

DOE OSTI2013

Effect of different breathing patterns in the same patient on stereotactic ablative body radiotherapy dosimetry for primary renal cell carcinoma: A case study

Stereotactic ablative body radiotherapy (SABR) for primary renal cell carcinoma (RCC) targets requires motion management strategies to verify dose delivery. This case study highlights the effect of a change in patient breathing amplitude on the dosimetry to organs at risk and target structures. A 73-year-old male patient was planned for receiving 26 Gy of radiation in 1 fraction of SABR for a left primary RCC. The patient was simulated with four-dimensional computed tomography (4DCT) and the tumor internal target volume (ITV) was delineated using the 4DCT maximum intensity projection. However, the initially planned treatment was abandoned at the radiation oncologist's discretion after pretreatment cone-beam CT (CBCT) motion verification identified a greater than 50% reduction in superior to inferior diaphragm motion as compared with the planning 4DCT. This patient was resimulated with respiratory coaching instructions. To assess the effect of the change in breathing on the dosimetry to the target, each plan was recalculated on the data set representing the change in breathing condition. A change from smaller to larger breathing showed a 46% loss in planning target volume (PTV) coverage, whereas a change from larger breathing to smaller breathing resulted in an 8% decrease in PTV coverage. ITV coverage was similarly reduced by 8% in both scenarios. This case study highlights the importance of tools to verify breathing motion prior to treatment delivery. 4D image guided radiation therapy verification strategies should focus on not only verifying ITV margin coverage but also the effect on the surrounding organs at risk.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Observation of laser ablation of silicon as a function of pulse length at constant fluence via time-resolved x-ray spectroscopy

We investigate the ablation of silicon as a function of laser pulse length at a constant fluence using time-resolved x-ray spectroscopy data obtained from OMEGA EP experiments at the University of Rochester's Laboratory for Laser Energetics. Our targets consisted of three-layer planar structures composed of Si (50 μm), Cu (25 μm), and SiO 2 (500 μm) layers. The Si layer was irradiated by a 351-nm laser with varying pulse widths of 250 ps, 500 ps, 1 ns, and 10 ns while maintaining a constant fluence of ~27.9 kJ/cm 2 . Electron temperatures and densities of the ablated plasma were determined by analyzing the time-resolved x-ray spectroscopy data through a comparison of experimental measurements with synthetic results obtained from Si atomic calculations in a steady state and non-local thermodynamic equilibrium. These calculations were computed using PrismSPECT. Additionally, radiation-hydrodynamics simulations with FLASH are used to generate simulated plasma-density and plasma-temperature profiles, which are then compared with the experimental measurements. Our analyses reveal that increasing the laser pulse length at a constant fluence results in a decrease in electron temperatures and densities. Furthermore, the longer pulses with lower intensities lead to deeper ablation regions before reaching the peak ablation but lower ionization balances in the silicon layer. Furthermore, these findings emphasize the critical role of laser pulse length in plasma ablation and shock generation for laser-impulse studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1985

Investigation on experimental techniques to detect, locate and quantify gear noise in helicopter transmissions

A robotic system to automate the detection, location, and quantification of gear noise using acoustic intensity measurement techniques has been successfully developed. Major system components fabricated under this grant include an instrumentation robot arm, a robot digital control unit and system software. A commercial, desktop computer, spectrum analyzer and two microphone probe complete the equipment required for the Robotic Acoustic Intensity Measurement System (RAIMS). Large-scale acoustic studies of gear noise in helicopter transmissions cannot be performed accurately and reliably using presently available instrumentation and techniques. Operator safety is a major concern in certain gear noise studies due to the operating environment. The man-hours needed to document a noise field in situ is another shortcoming of present techniques. RAIMS was designed to reduce the labor and hazard in collecting data and to improve the accuracy and repeatability of characterizing the acoustic field by automating the measurement process. Using RAIMS a system operator can remotely control the instrumentation robot to scan surface areas and volumes generating acoustic intensity information using the two microphone technique. Acoustic intensity studies requiring hours of scan time can be performed automatically without operator assistance. During a scan sequence, the acoustic intensity probe is positioned by the robot and acoustic intensity data is collected, processed, and stored.

Flanagan, P. M.↗

NASA NTRS2008

Validation of a Remote Sensing Based Index of Forest Disturbance Using Streamwater Nitrogen Data

Vegetation disturbances are known to alter the functioning of forested ecosystems by contributing to export ('leakage') of dissolved nitrogen (N), typically nitrate-N, from watersheds that can contribute to acidification of acid-sensitive streams, leaching of base cations, and eutrophication of downstream receiving waters. Yet, at a landscape scale, direct evaluation of how disturbance is linked to spatial variability in N leakage is complicated by the fact that disturbances operate at different spatial scales, over different timescales, and at different intensities. In this paper we explore whether data from synoptic streamwater surveys conducted in an Appalachian oak-dominated forested river basin in western MD (USA) can be used to test and validate a scalable, synthetic, and integrative forest disturbance index (FDI) derived from Landsat imagery. In particular, we found support for the hypothesis that the interannual variation in spring baseflow total dissolved nitrogen (TDN) and nitrate-N concentrations measured at 35 randomly selected stream stations varied as a linear function of the change in FDI computed for the corresponding set of subwatersheds. Our results demonstrate that the combined effects of forest disturbances can be detected using synoptic water quality data. It appears that careful timing of the synoptic baseflow sampling under comparable phenological and hydrometeorological conditions increased our ability to identify a forest disturbance signal.

Eshleman, Keith N.↗

NASA NTRS2015

Modeling and Analysis of Realistic Fire Scenarios in Spacecraft

An accidental fire inside a spacecraft is an unlikely, but very real emergency situation that can easily have dire consequences. While much has been learned over the past 25+ years of dedicated research on flame behavior in microgravity, a quantitative understanding of the initiation, spread, detection and extinguishment of a realistic fire aboard a spacecraft is lacking. Virtually all combustion experiments in microgravity have been small-scale, by necessity (hardware limitations in ground-based facilities and safety concerns in space-based facilities). Large-scale, realistic fire experiments are unlikely for the foreseeable future (unlike in terrestrial situations). Therefore, NASA will have to rely on scale modeling, extrapolation of small-scale experiments and detailed numerical modeling to provide the data necessary for vehicle and safety system design. This paper presents the results of parallel efforts to better model the initiation, spread, detection and extinguishment of fires aboard spacecraft. The first is a detailed numerical model using the freely available Fire Dynamics Simulator (FDS). FDS is a CFD code that numerically solves a large eddy simulation form of the Navier-Stokes equations. FDS provides a detailed treatment of the smoke and energy transport from a fire. The simulations provide a wealth of information, but are computationally intensive and not suitable for parametric studies where the detailed treatment of the mass and energy transport are unnecessary. The second path extends a model previously documented at ICES meetings that attempted to predict maximum survivable fires aboard space-craft. This one-dimensional model implies the heat and mass transfer as well as toxic species production from a fire. These simplifications result in a code that is faster and more suitable for parametric studies (having already been used to help in the hatch design of the Multi-Purpose Crew Vehicle, MPCV).

life support↗

DOE OSTI2015

A Ku band pulsed electron paramagnetic resonance spectrometer using an arbitrary waveform generator for quantum control experiments at millikelvin temperatures

We present a 17 GHz (Ku band) arbitrary waveform pulsed electron paramagnetic resonance spectrometer for experiments down to millikelvin temperatures. The spectrometer is located at room temperature, while the resonator is placed either in a room temperature magnet or inside a cryogen-free dilution refrigerator; the operating temperature range of the dilution unit is from ca. 10 mK to 8 K. This combination provides the opportunity to perform quantum control experiments on electron spins in the pure-state regime. At 0.6 T, spin echo experiments were carried out using γ-irradiated quartz glass from 1 K to 12.3 mK. With decreasing temperatures, we observed an increase in spin echo signal intensities due to increasing spin polarizations, in accordance with theoretical predictions. Through experimental data fitting, thermal spin polarization at 100 mK was estimated to be at least 99%, which was almost pure state. Next, to demonstrate the ability to create arbitrary waveform pulses, we generate a shaped pulse by superposing three Gaussian pulses of different frequencies. The resulting pulse was able to selectively and coherently excite three different spin packets simultaneously—a useful ability for analyzing multi-spin system and for controlling a multi-qubit quantum computer. By applying this pulse to the inhomogeneously broadened sample, we obtain three well-resolved excitations at 8 K, 1 K, and 14 mK.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Analysis and Benchmarking of feature reduction for classification under computational constraints

Abstract Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1991

Simulation of intense microwave pulse propagation in air breakdown environment

An experiment is conducted to examine the tail erosion phenomenon which occurs to an intense microwave pulse propagating in air breakdown environment. In the experiment, a 1 MW microwave pulse (1.1 microsec) is transmitted through a large plexiglas chamber filled with dry air at about 1-2 torr pressure. Two different degrees of tail erosion caused by two different mechanisms are identified. This experimental effort leads to the understanding of the fundamental behavior of tail erosion and provides a data base for validating the theoretical model. A theoretical model based on two coupled partial differential equations is established to describe the propagation on an intense microwave pulse in air breakdown environment. One is derived from the Poynting theorem, and the other one is the rate equation of electron density. A semi-empirical formula of the ionization frequency is adopted for this model. A transformation of these two equations to local time frame of reference is introduced so that they can be solved numerically with considerably reduced computation time. This model is tested by using it to perform the computer simulation of the experiment. The numerical results are shown to agree well with the experimental results.

Kuo, S. P.↗

NASA NTRS2023

Compact Lightweight Aerial Sensor System (CLASSy)

In the wake of increasingly intense wildfires, innovative solutions are imperative to enhance wildfire mitigation strategies. Current technological integrations have hit a communicative limit. Between limited flight time, computational expenses as well as financial expenses, there is a hole in the market for an effective, low-tech, and disposable solution. The Compact Lightweight Aerial Sensor System (CLASSy) is designed to revolutionize active disaster operations through comprehensive decision support. CLASSy consists of a lightweight launch mechanism and a flight body equipped with a sensor package and parachute. The assembly integrates sensor networks with data analytics to provide real-time, high-resolution information to incident commanders, directly facilitating decision-making and resource allocation. Infrared imagery and temperature differentials are processed and analyzed throughout flight, offering valuable insights into fire behavior, hotspot detection, and fire spread trajectories. CLASSy is intended to meet a variety of natural disaster mitigation needs through its variable launch height and disposability. CLASSy’s goal is to assist wildfire fighting without taking up any human or material resources. As a result, CLASSy is as lightweight as possible, easily expendable, inexpensive to manufacture, and only requires one operator for effective use. CLASSy’s integrated sensor suite, real-time analytics, and closed loop active communications empower firefighting teams to proactively address wildfire challenges. As the frequency of wildfires continues to rise, technological innovations like CLASSy are crucial to effective wildfire management systems.

Kyleigh Anderson↗

DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗

DOE OSTI2022

Quantifying Dynamic Signal Spread in Real-Time High-Energy X-ray Diffraction

Abstract Measured intensity in high-energy monochromatic X-ray diffraction (HEXD) experiments provides information regarding the microstructure of the crystalline material under study. The location of intensity on an areal detector is determined by the lattice spacing and orientation of crystals so that changes in the heterogeneity of these quantities are reflected in the spreading of diffraction peaks over time. High temporal resolution of such dynamics can now be experimentally observed using technologies such as the mixed-mode pixel array detector (MM-PAD) which facilitates in situ dynamic HEXD experiments to study plasticity and its underlying mechanisms. In this paper, we define and demonstrate a feature computed directly from such diffraction time series data quantifying signal spread in a manner that is correlated with plastic deformation of the sample. A distinguishing characteristic of the analysis is the capability to describe the evolution from the distinct diffraction peaks of an undeformed alloy sample through to the non-uniform Debye–Scherrer rings developed upon significant plastic deformation. We build on our previous work modeling data using an overcomplete dictionary by treating temporal measurements jointly to improve signal spread recovery. We demonstrate our approach in simulations and on experimental HEXD measurements captured using the MM-PAD. Our method for characterizing the temporal evolution of signal spread is shown to provide an informative means of data analysis that adds to the capabilities of existing methods. Our work draws on ideas from convolutional sparse coding and requires solving a coupled convex optimization problem based on the alternating direction method of multipliers.

Engineering↗

DOE OSTI2017

PyDREAM: high-dimensional parameter inference for biological models in python

Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Cho, B↗