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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Queue wait time prediction in high performance computing (HPC) systems

High Performance Computing (HPC) systems are critical enablers for groundbreaking scientific research across various domains. Efficient resource allocation, facilitated by job scheduling, is paramount for maximizing the utilization of HPC systems. However, the variability in wait times for queued jobs poses challenges for users, necessitating accurate job wait time estimation. This paper explores the influence of job characteristics, including job size (the number of nodes requested and walltime), the queue to which the job is submitted and other resource requirements, on job wait times in leadership-class HPC systems. Focusing on the Theta Cray XC40 and Polaris machines at Argonne National Laboratory, the study evaluates the performance of different supervised learning algorithms in predicting job wait times. It also evaluates the impact of data preprocessing, including outlier detection, Principal Component Analysis (PCA), and feature selection, on the performance of wait time prediction models. The findings reveal insights into the relationship between job characteristics and wait times, offering a foundation for optimizing resource allocation and enhancing user experience. The methodologies and tools developed in this study are adaptable to other leadership-class HPC systems, providing a valuable contribution to the broader HPC community aiming to improve job scheduling efficiency and user satisfaction.

Okafor, Nwamaka↗
DOE OSTI2026

Noise-aware optimization in nominally identical manufacturing and measuring systems for high-throughput parallel workflows

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or enforce generic robustness, the proposed framework explicitly determines whether shared optimization across devices is appropriate based on the degree of inter-device noise heterogeneity. This enables improved performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability, along with improved convergence stability and solution quality through the selection of the appropriate optimization strategy based on the degree of inter-device noise heterogeneity. Overall, this framework establishes a general approach for precision- and resource-aware optimization in scalable, automated experimental platforms, demonstrated here on a representative multi-device 3D printing case study.

Schenk, Christina↗
DOE OSTI2019

Automated high-throughput tensile testing reveals stochastic process parameter sensitivity

The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. As a result, it is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimal binning of correlated measurements

Experimental measurements are commonly represented on a discrete grid, requiring a balance between granularity and statistical noise. Two strategies have traditionally been used to improve such representations: selecting an appropriate bin width to control discretization error and applying kernel-based smoothing to suppress fluctuations. Despite their shared goal, these approaches have largely developed independently, without a unified statistical description of how discretization and correlation jointly determine measurement precision. Here, we extend the discussion of optimal interval averaging to a correlation-aware setting by Gaussian process regression, which explicitly accounts for correlations among neighboring bins. Starting from first principles, we derive the mean-squared error of discretized measurements and obtain closed-form asymptotic expressions for the optimal bin width and correlation length. When recast in reduced variables, the theory reveals distinct universal scaling laws governing the error in the correlation-free and correlation-controlled regimes. Characterized by intrinsically smooth intensity profiles and counting-based statistics, neutron scattering measurements are well suited for demonstrating the enhanced error contraction enabled by inter-bin correlations. We show that such improvement is achievable over the experimentally accessible Q-range and across multiple instruments and material systems. These results show that explicitly accounting for correlations systematically reshapes the limits of precision in discretized, noise-limited measurements. More broadly, the framework provides a transferable statistical foundation for optimizing data representation, inference, and experimental design across the physical and data sciences.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2016

String duality transformations in f(R) gravity from Noether symmetry approach

We select f(R) gravity models that undergo scale factor duality transformations. As a starting point, we consider the tree-level effective gravitational action of bosonic String Theory coupled with the dilaton field. This theory inherits the Busher's duality of its parent String Theory. Using conformal transformations of the metric tensor, it is possible to map the tree-level dilaton-graviton string effective action into f(R) gravity, relating the dilaton field to the Ricci scalar curvature. Furthermore, the duality can be framed under the standard of Noether symmetries and exact cosmological solutions are derived. Using suitable changes of variables, the string-based f(R) Lagrangians are shown in cases where the duality transformation becomes a parity inversion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2020

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. Additionally, these techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Optimization of scat detection methods for a social ungulate, the wild pig, and experimental evaluation of factors affecting detection of scat

Collection of scat samples is common in wildlife research, particularly for genetic capture-mark-recapture applications. Due to high degradation rates of genetic material in scat, large numbers of samples must be collected to generate robust estimates. Optimization of sampling approaches to account for taxa-specific patterns of scat deposition is, therefore, necessary to ensure sufficient sample collection. While scat collection methods have been widely studied in carnivores, research to maximize scat collection and noninvasive sampling efficiency for social ungulates is lacking. Further, environmental factors or scat morphology may influence detection of scat by observers. We contrasted performance of novel radial search protocols with existing adaptive cluster sampling protocols to quantify differences in observed amounts of wild pig ( Sus scrofa ) scat. We also evaluated the effects of environmental (percentage of vegetative ground cover and occurrence of rain immediately prior to sampling) and scat characteristics (fecal pellet size and number) on the detectability of scat by observers. We found that 15- and 20-m radial search protocols resulted in greater numbers of scats encountered than the previously used adaptive cluster sampling approach across habitat types, and that fecal pellet size, number of fecal pellets, percent vegetative ground cover, and recent rain events were significant predictors of scat detection. Our results suggest that use of a fixed-width radial search protocol may increase the number of scats detected for wild pigs, or other social ungulates, allowing more robust estimation of population metrics using noninvasive genetic sampling methods. Further, as fecal pellet size affected scat detection, juvenile or smaller-sized animals may be less detectable than adult or large animals, which could introduce bias into abundance estimates. In conclusion, knowledge of relationships between environmental variables and scat detection may allow researchers to optimize sampling protocols to maximize utility of noninvasive sampling for wild pigs and other social ungulates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

The Molten Salt Reactor (MSR) represents a significant innovation in nuclear technology, offering several operational and safety benefits over traditional solid-fuel reactors. However, MSRs face uncertainties in waste management due to their flexible designs and variable waste compositions. To address these challenges, we propose a visualization platform that illustrates solutions and performance predictions for various waste management strategies, enhancing user experience and improving strategy and communication. Immersive visualizations are widely used in the nuclear industry for training, simulation, and safety enhancement. Our project aims to develop a visualization platform incorporating virtual reality (VR) technologies to illustrate MSR characteristics immediately following reactor shutdown. This immersive simulation will allow users to interact, explore, and understand different waste management strategies. The platform will display MSR reactor characterizations, including nuclide decay, salt solidification, and corrosion, which are crucial for assessing and selecting backend management strategies. Using the Meta Quest 3 VR headset with Unity software, our platform will provide real-scale visualizations, enabling users to experience and evaluate designs and plans as if they were physically present. This user-friendly interface will make complex data accessible and understandable for non-domain experts, aiding in decision-making for MSR waste management. Our proposed visualization workflow can be applied to other nuclear reactors, assisting in the design and planning of waste management strategies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Additional value of systolic wall thickening in myocardial stunning evaluated by stress-rest gated perfusion SPECT

Aim and patients: The aim of the present study is to evaluate the additional value of systolic wall thickening to myocardial perfusion in diagnosing myocardial stunning in patients with angiography proven coronary artery disease. We selected 91 ischemic patients (82 males; mean age 59.7 ± 10.3) with CAD documented by angiography. Ischemia was defined as a summed difference score ≥5. All patients underwent a 2-day gated perfusion SPECT protocol. The patients received a dose of 740 MBq of {sup 99m}Tc-tetrofosmin after stress and at rest. Treadmill maximal exercise tests were performed on all patients. Results: The post-stress LVEF was significantly lower than rest LVEF (48.1% ± 10.3% vs 50.3% ± 10.7%; P = .0001). The wall thickening summed difference score was 4.44 ± 4.13 (P = .0001). At a multivariate regression analysis, only WT-SDS as independent variable was significantly correlated with myocardial ischemia (SDS). We also divided patients according to SDS in those with mild (SDS < 8) and severe (SDS ≥ 8) ischemia. WT-SDS, but not ∆LVEF, was significantly different between groups. Conclusions: WT-SDS, more than the depression in the global function (∆LVEF) of the left ventricle, correlates with the degree of ischemia and better identifies, when present, the stunning phenomenon.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Identification, expansion and characterization of cancer cells with stem cell properties from head and neck squamous cell carcinomas

Head and neck squamous cell carcinoma (HNSCC) is a major public health concern. Recent data indicate the presence of cancer stem cells (CSC) in many solid tumors, including HNSCC. Here, we assessed the stem cell (SC) characteristics, including cell surface markers, radioresistance, chromosomal instability, and in vivo tumorigenic capacity of CSC isolated from HNSCC patient specimens. We show that spheroid enrichment of CSC from early and short-term HNSCC cell cultures was associated with increased expression of CD44, CD133, SOX2 and BMI1 compared with normal oral epithelial cells. On immunophenotyping, five of 12 SC/CSC markers were homogenously expressed in all tumor cultures, while one of 12 was negative, four of 12 showed variable expression, and two of the 12 were expressed heterogeneously. We showed that irradiated CSCs survived and retained their self-renewal capacity across different ionizing radiation (IR) regimens. Fluorescence in situ hybridization (FISH) analyses of parental and clonally-derived tumor cells revealed different chromosome copy numbers from cell to cell, suggesting the presence of chromosomal instability in HNSCC CSC. Further, our in vitro and in vivo mouse engraftment studies suggest that CD44+/CD66− is a promising, consistent biomarker combination for HNSCC CSC. Overall, our findings add further evidence to the proposed role of HNSCC CSCs in therapeutic resistance. - Highlights: • Spheroid enrichment selects cancer stem cells (CSC) from head & neck tumors (HNSCC). • Compared to normal epithelial cells, isolated CSC express increased SC/CSC markers. • Isolated CSC display enhanced radioresistance, clonogenicity and tumorigenicity. • HNSCC CSC express chromosomal instability. • CD44+/CD66− is a promising, consistent biomarker for HNSCC CSC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Reduced Order Model to Predict Dispersion of Flammable Refrigerant into a Space

As the HVAC&R industry mobilizes to deploy more low-GWP refrigerants, relevant standards are being continually reviewed and updated. Those include the general safety standards ISO 5149 and ASHRAE 15, and the equipment standards IEC and UL. The standards systematically set the allowable maximum amount of refrigerant that should be used in different equipment types and different applications. To do so, they rely on predictions of how a leaked refrigerant mass will disperse into a space. Dispersion characteristics, such as total flammable volume and its residence time, determine the risk associated with the presence of the flammable refrigerant. The standards have included provisions for the use of flammable refrigerants for approximately two decades. They relied on limited analytical analyses and test cases in their development. Dispersion of a refrigerant into a space is complex. Computational fluid dynamics (CFD) are the most accurate in predicting a given problem. However, CFD is computationally expensive and requires specialized expertise and resources and is not suitable for use by standards development working group as prediction tool. This paper presents the development of a reduced order model (ROM) that predicts the key dispersion characteristics relevant to the dispersion of a leaked refrigerant into a space for any combination of input variables. The inputs are the refrigerant release height, the total released refrigerant mass and its release flow rate, the refrigerant molecular weight, the ventilation flow rate, the floor area and height of the space, recirculation air flow rate, and the tightness of the space. The outputs are histograms of volume fraction of the room in prescribed concentration bins and the total mass of the refrigerant in each bin normalized by the total refrigerant charge at 13 prescribed simulation time stamps between 1 and 900 seconds. The ROM is constructed from a set of CFD simulations with carefully chosen combinations of input parameters. The selection if done using a multidimensional sparse grid which is a generalization of the classical tensor approach but offers additional flexibility and thus can be more carefully tuned towards a specific model. The tuning is done to improve the accuracy, measured in the difference between the output values of the ROM and the CFD model, while minimizing the computational cost, measured in number of CFD simulations which is orders of magnitude more expensive than the processing the training data.

Edwards, Dean↗
DOE OSTI2024

Decomposing a renewable energy design and dispatch model

We address a mixed-integer linear programming model which selects a cost-minimizing set of available technologies with which to design a renewable energy system and prescribe their associated dispatch decisions. Realistically sized instances of such models pose computational challenges. To this end, we develop a Lagrangian heuristic based on a decomposition methodology which partitions the model into blocks and optimizes these more manageable, smaller subproblems. It also provides a lower bound to assess solution quality. In conclusion, we apply this methodology to the National Renewable Energy Laboratory's Renewable Energy Integration and Optimization (REopt TM ) model to generate near-optimal solutions to realistic instances containing, on average, approximately 300,000 variables and at least as many constraints, with a mean 30% optimality gap improvement using a five-minute solution time limit, compared to directly solving the original monolith.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Targeting tumor cells based on Phosphodiesterase 3A expression

Highlights: • PDE3A mRNA overexpression correlates to PDE3A protein overexpression. • High levels of PDE3A protein can be detected in clinical samples using IHC. • Primary cells from subgroup of patients with high PDE3A respond to PDE3 inhibitors. • PDE3A is overexpressed in subsets of patient derived samples from various tumors. • Gastrointestinal stromal tumors show the highest expression of PDE3A. We and others have previously reported a correlation between high phosphodiesterase 3 A (PDE3A) expression and selective sensitivity to phosphodiesterase (PDE) inhibitors. This indicates that PDE3A could serve both as a drug target and a biomarker of sensitivity to PDE3 inhibition. In this report, we explored publicly available mRNA gene expression data to identify cell lines with different PDE3A expression. Cell lines with high PDE3A expression showed marked in vitro sensitivity to PDE inhibitors zardaverine and quazinone, when compared with those having low PDE3A expression. Immunofluorescence and immunohistochemical stainings were in agreement with PDE3A mRNA expression, providing suitable alternatives for biomarker analysis of clinical tissue specimens. Moreover, we here demonstrate that tumor cells from patients with ovarian carcinoma show great variability in PDE3A protein expression and that level of PDE3A expression is correlated with sensitivity to PDE inhibition. Finally, we demonstrate that PDE3A is highly expressed in subsets of patient tumor cell samples from different solid cancer diagnoses and expressed at exceptional levels in gastrointestinal stromal tumor (GIST) specimens. Importantly, vulnerability to PDE3 inhibitors has recently been associated with co-expression of PDE3A and Schlafen family member 12 (SLFN12). We here demonstrate that high expression of PDE3A in clinical specimens, at least on the mRNA level, seems to be frequently associated with high SLFN12 expression. In conclusion, PDE3A seems to be both a promising biomarker and drug target for individualized drug treatment of various cancers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Approach for Configuring a Standardized Vessel for Processing Radioactive Waste Slurries

A standardized vessel design is being considered at the Waste Treatment and Immobilization Plant (WTP) that is under construction at Hanford, Washington. The standardized vessel design will be used for storing, blending, and chemical processing of slurries that exhibit a variable process feed including Newtonian to non-Newtonian rheologies over a range of solids loadings. Developing a standardized vessel is advantageous and reduces the testing required to evaluate the performance of the design. The objectives of this paper are to: 1) present a design strategy for developing a standard vessel mixing system design for the pretreatment portion of the waste treatment plant that must process rheologically and physically challenging process streams, 2) identify performance criteria that the design for the standard vessel must satisfy, 3) present parameters that are to be used for assessing the performance criteria, and 4) describe operation of the selected technology. Vessel design performance will be assessed for both Newtonian and non-Newtonian simulants which represent a range of waste types expected during operation. Desired conditions for the vessel operations are the ability to shear the slurry so that flammable gas does not accumulate within the vessel, that settled solids will be mobilized, that contents can be blended, and that contents can be transferred from the vessel. A strategy is presented for adjusting the vessel configuration to ensure that all these conditions are met.

pulse jet mixing↗
DOE OSTI2020

VALVE TIMING AND AREA PROFILE SELECTION FOR HYDRAULIC PUMPS AND MOTORS

A significant amount of research has been conducted to select valve timing and area profiles that create efficient and quiet hydraulic pumps and motors. Numerous active valve architectures have been modeled and optimized, but the rationale for the final solution is often unclear. The solution is usually highly dependent on the modeled valve geometry constraints and the duty cycle of the pump or motor for which the valve was optimized. This paper first presents an ideal valve area profile and what compromises are made to design for a working pressure and speed range. The ideal valve area profile is resolved using a piston-cylinder model with a pressure dependent bulk modulus and variable valve openings. The piston-cylinder is first modeled as a pump with passive (check) valves and a specified pressure drop across the valves to determine the ideal area profile. A generalization of the ideal timing is demonstrated for positive pressure differential and positive rotation direction, also known as the first quadrant. The effects on valve timing when varying pressure are shown for the first quadrant, but the trends can be extrapolated for all quadrants of operation. The ideal valve profile is implemented as fixed valve timing, meaning the valve area is only a function of the timing angle, in a pump-motor. The pump-motor is simulated in one rotation direction through a pressure range. Performance is high in pumping operation, but when the pressure differential is reversed, cylinder pressure spikes ensue. Two strategies to modify an ideal valve area profile are presented: timing grooves and a pressure shifted valve timing. Timing grooves reduce pressure spikes and cavitation in the cylinder but generally increase throttling losses. A pressure shifted valve timing has lower throttling energy losses, making it the favored solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Bayesian State-Space Modeling Framework for Understanding and Predicting Golden Eagle Movements Using Telemetry Data

Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.

Bayesian modeling↗