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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

BigData Express: Toward Predictable, Schedulable, and High-Performance Data Transfer [Slides]

BigData Express: a schedulable, predictable, and high-performance data transfer service. A peer-to-peer, scalable, and extensible data transfer model; A visually appealing, easy-to-use web portal; A high-performance data transfer engine; On-demand provisioning of end-to-end network paths with guaranteed QoS; Robust and flexible error handling; CILogon-based security (certificate version); A rich set of REST APIs to support scientific workflows

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

An Online Prototype Toolset for Predicting and Optimizing P&T Performance (FY23 Status Report)

A new web-based toolset is being developed to support ongoing remediation optimization efforts and implementation of an adaptive site management strategy for the 200 West Area Pump-and-Treat (P&T) system at the Hanford Site. This toolset, comprising the well performance index tool and the well optimization pre-screening tool, will offer a user-friendly interface to predict and optimize the P&T well network’s performance at a preliminary level. Efforts in fiscal year (FY) 2023 focused on three main components: updating the existing deep learning model for predicting P&T performance, designing and developing a prototype of a web-based performance index tool, and initiating the conceptual design of the well optimization pre-screening tool. The well performance index tool is based on a pre-trained deep learning model that allows users to select a target contaminant and well screen length, then visualize the predicted performance of potential new wells across the site. The well optimization pre-screening tool includes two separate modules: the pre-computed scenario viewer, which organizes and visualizes offline optimization simulation results, and the quick analysis module, which provides real-time model prediction using user-specified well locations. In FY24, the plan is to add web-based applications to SOCRATES for both the well performance prediction tool and the optimization prescreening tool, with accompanying user and theory guides. These tools are intended to enable an accessible, easily applied, and transparent approach to remedy planning and decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Diolkos: Improving ethernet throughput through dynamic port selection

In large networked systems, a sudden increase in traffic could slowdown the network significantly, impacting network quality for multiple users. We present Diolkos, a system that leverages smart switches to dynamically re-reroute data flows in response to drops in performance. In contrast to other techniques, our tool predicts the future throughput at each port in a switch if a data flow were to be sent through it, and updates which port should be taken to maximize throughput. We use several techniques to predict network switch performance on a software defined network (SDN) mimicking topologies commonly found in datacenters. Experimentally, we demonstrate the effectiveness of choosing a port to send flows through based on predicted performance. We found that using a distributed predictive technique achieves a 24% improvement over using a traditional heuristic technique.

port selection, performance enhancement, Neural Ne↗
DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗
DOE OSTI2020

Design optimization of semiconductor piezoresistors with Schottky diode contacts

A modeling theory is developed to predict the performance of piezoresistors which incorporate Schottky diode electrical contacts. This new theory allows the design of high performance gauges which can be fabricated using NonLithographically-Based Microfabrication (NLBM) techniques. These semiconductor piezoresistors can be designed in customizable sizes and fabricated in parallel in order to integrate position sensing into MEMS flexural positioners. Customizable nanopositioning platforms will enable advances in a range of nano-scale fabrication and metrology applications. A semiconductor piezoresistor with Schottky diode contacts was fabricated and attached to a titanium flexure. This device is shown to match predicted electrical performance within about 8% and to show a gauge factor of 116, within 2% of the predicted value. Performance limits for Schottky diode semiconductor piezoresistors are identified to be about 127dB full noise dynamic range for a quarter bridge over a 10kHz sensor bandwidth on a 600µm width titanium flexure, making them ideal for sensing on meso-/micro-scale flexural positioners. Methods of reaching these performance bound are suggested and their impact on the sensor dynamic range are studied.

42 ENGINEERING↗
DOE OSTI2020

Deffe: A Data-efficient Framework for Performance Characterization in Domain-Specific Computing

Predicting workload performance is a crucial task for many architecture and system research. In this paper we present De￿e, a framework to estimate the workload performances under varying architectural configurations. The infrastructure component of Deffe is based on scalable and easy-to-use open-source software components. By casting the performance modeling as transfer learning tasks, the modeling component of Deffe can leverage the learned knowledge on one workload, and “transfer” it to a new workload. Extensive experimental results show that the method can achieve superior testing accuracy with an effective reduction of 32-80x in terms of the amount of required training data.

Liu, Frank↗
DOE OSTI2025

Mortality correlates with tree functional traits across a wood density gradient in the Central Amazon

Introduction: Understanding the mechanisms of tree mortality in tropical ecosystems remains challenging, in part due to the high diversity of tree species and the inherently stochastic nature of mortality. Plant functional traits offer a mechanistic link between plant physiology and performance, yet their ability to predict growth and mortality remains poorly understood. Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon. Methods: Seventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Traits of the individual trees were related to stand-level growth and mortality rates collected periodically over 30 years from nearby permanent inventory plots. Results: Higher wood density was associated with smaller leaf size, lower foliar base cations, lower stem water content and sapwood fraction, in agreement with the fast-slow plant economics spectrum. Lower wood density was associated with more acquisitive characteristics with greater hydraulic capacity and foliar nutrient concentrations, correlating with greater growth and mortality rates. Discussion: Our results show that lower wood density is part of a coordinated suite of traits linked to high resource acquisition, fast growth, and increased mortality risk, providing a functional framework for predicting species performance and forest vulnerability under future climate stress.

demographics↗
DOE OSTI2023

Optimizing Communication in 2D Grid-Based MPI Applications at Exascale

The new reality of exascale computing faces many challenges in achieving optimal performance on large numbers of nodes. A key challenge is the efficient utilization of the message-passing interface (MPI), a critical component for process communication. This paper explores communication optimization strategies to harness the GPU-accelerated architectures of these supercomputers. We focus on MPI applications where processors form a two-dimensional process grid, a common arrangement in applications involving dense matrix operations. This configuration offers a unique opportunity to implement innovative strategies to improve performance and maintain effective load distribution. We study two applications— Dist-FW (Apsp:all-pair-shortest-path) and HPL-MxP (LU factorization with Mixed precision)—on two accelerated systems: Summit (IBM Power and NVIDIA V100) and Frontier (AMD EPYC and MI250X). These supercomputers are operated by the Oak Ridge Leadership Computing Facility (OLCF) and are currently ranked #1 and #5 on the Top500 list. We show how to scale up both applications to exascale levels and tackle the MPI challenges related to implementation, synchronization, and performance. We also compare the performance of several communication strategies at an unprecedented scale. Accurately predicting application performance becomes crucial for cost reduction as the computational scale grows. To address this, we suggest a hyperbolic model as a better alternative to the traditional one-sided asymptotic model for predicting future application performance at such large scales.

Lu, Hao↗
DOE OSTI2022

NEAR CRITICAL POINT TESTING AND PERFORMANCE RESULTS OF A SCO2 COMPRESSOR FOR A 10MWE BRAYTON CYCLE

Development and commercial acceptance of sCO2 Brayton cycles for power generation applications are growing rapidly as they offer performance advantages over other cycles. To maximize the cycle performance, the compressors are designed to operate near the critical point of the working fluid. At the critical point the fluid properties change rapidly with variations in inlet conditions. This makes it challenging to both accurately predict the performance and guarantee the operability of the compressor, as the behavior is affected by these slight variations in inlet conditions. A full scale 1st stage main compressor for a 10MWe-Class recompression Brayton cycle was built and tested to validate performance and operability in this unique operating range. The compressor was tested in a laboratory environment with additional instrumentation, beyond what is required by PTC-10, to minimize the uncertainty in the measured performance. Complete constant speed characteristics were collected at multiple supercritical points, operating at constant inlet conditions for each speed line covering a range of compressor inlet densities from 400 to 600 kg/m3. Variations in the compressor stage efficiency and choke margin were observed, and the overall operability and stability of the compressor in response to changes in operating condition were also monitored. The compressor was shown to have excellent performance that closely matched the original design prediction. The performance at various inlet conditions showed minimal change in isentropic head coefficient at the design flow, but did show some variation in efficiency and choke margin across the map. These changes in performance were observed to be minimal, and did not affect the stable operation of the compressor. The results demonstrate that a commercial scale sCO2 compressor can operate near the critical point and achieve the high levels of performance and stability required for power generation applications.

pelton, rob↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas

Time-dependent, predictive simulations were performed with the 1.5D tokamak integrated modeling code TRANSP on a large set of well-analyzed, high performing discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate how well modern reduced transport models can reproduce experimentally observed temperature profiles in spherical tokamaks. Overall, it is found that simulations using the Multi-Mode Model (MMM) more consistently agree with the NSTX observations than those using the Trapped Gyro-Landau Fluid (TGLF) model, despite TGLF requiring orders of magnitude greater computational cost. When considering all examined discharges, MMM has median overpredictions of electron temperature (T e ) and ion temperature (T i ) profiles of 28% and 27%, respectively, relative to the experiment. TGLF overpredicts T e by 46%, with much larger variance than MMM, and underpredicts T i by 25%. As the ratio of kinetic to magnetic field pressure (β) is increased across NSTX discharges, TGLF predicts lower T e and significant flattening of the T i profile, conflicting with NSTX observations. When using an electrostatic version of TGLF, both T e and T i are substantially overpredicted, underscoring the importance of electromagnetic turbulence in the high β spherical tokamak regime. Additionally, calculations with neural net surrogate models for TGLF were performed outside of TRANSP with a time slice flux matching transport solver, finding better agreement with experiment than the TRANSP simulations, highlighting the impact of different transport solvers and simulation techniques. Furthermore, the reasonable agreement with experiment of temperature profiles predicted by MMM motivates a more detailed examination of the sensitivities of the TRANSP simulations with MMM to different NSTX plasma regimes in a companion paper (Lestz et al Plasma Phys. Control. Fusion 67 105030), in preparation for self-consistent, time-dependent predictive modeling of NSTX-U scenarios.

Lestz, J. B. [General Atomics, San Diego, CA (Unit↗
DOE OSTI2021

Predictions of core plasma performance for the SPARC tokamak

SPARC is designed to be a high-field, medium-size tokamak aimed at achieving net energy gain with Ion Cyclotron Range-of-Frequencies (ICRF) as its primary auxiliary heating mechanism. Empirical predictions with conservative physics indicate that SPARC baseline plasmas would reach Q~11, well above its mission objective of Q>2. To build confidence that SPARC will be successful, physics-based integrated modeling has also been performed. The TRANSP code coupled with the theory-based TGLF turbulence model and EPED predictions for pedestal stability find that Q~9 is attainable in standard H-mode operation and confirms Q>2 operation is feasible even with adverse assumptions. In this analysis, ion cyclotron waves are simulated with the full wave TORIC code and alpha heating is modeled with the Monte-Carlo fast ion NUBEAM module. Detailed analysis of expected turbulence regimes with linear and nonlinear CGYRO simulations is also presented, demonstrating that profile predictions with the TGLF reduced model are in reasonable agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Predictions of core plasma performance for the SPARC tokamak

SPARC is designed to be a high-field, medium-size tokamak aimed at achieving net energy gain with ion cyclotron range-of-frequencies (ICRF) as its primary auxiliary heating mechanism. Empirical predictions with conservative physics indicate that SPARC baseline plasmas would reach $\textit{Q}$ ≈ 11, which is well above its mission objective of $\textit{Q}$ > 2. To build confidence that SPARC will be successful, physics-based integrated modelling has also been performed. The TRANSP code coupled with the theory-based trapped gyro-Landau fluid (TGLF) turbulence model and EPED predictions for pedestal stability find that $\textit{Q}$ ≈ 9 is attainable in standard H-mode operation and confirms $\textit{Q}$ > 2 operation is feasible even with adverse assumptions. In this analysis, ion cyclotron waves are simulated with the full wave TORIC code and alpha heating is modelled with the Monte–Carlo fast ion NUBEAM module. Detailed analysis of expected turbulence regimes with linear and nonlinear CGYRO simulations is also presented, demonstrating that profile predictions with the TGLF reduced model are in reasonable agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Deep learning models for interpretation of point of care ultrasound in military working dogs

Introduction: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs. Methods: Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions. Results: Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation. Discussion: Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

In-situ magnetic alignment model for additive manufacturing of anisotropic bonded magnets

In this work, we report a mathematical framework which predicts the degree of alignment (DoA) in an in-situ aligned additively manufactured 3D printed bonded magnets. A multiphysics model is developed which couples the harmonious interactions of magnetic particles in a viscous flowing polymer under the presence of an external magnetic field. The hydrodynamic fluid-particle interaction is paired with the magnetophoretic force to predict the particle trajectory and distribution during extrusion through a 3D printer nozzle. Succeeding the force balance, a magnetohydrodynamic torque equilibrium analysis is performed to predict the net-orientation of the magnetic particles as a function of the applied field strength, viscous forces, and particle-to-particle interactions (P2P). Experimental validation of the DoA predictions is performed using 65 vol% Nd-Fe-B+Sm-Fe-N in Nylon12 (DoA exp = 0.620 and DoA theory = 0.686) and 15 vol% Sm-Co in PLA (DoA exp = 0.830 and DoA theory = 0.863). A parametric analysis is performed to analyze the effect of operating and design parameters like alignment field strength, magnetic loading fraction, extrusion load, and particle size. The model predicts a competing behavior between particle-fluid and particle-particle interactions under the presence of an applied field. Lastly, the model provides a framework to efficiently predict the DoA in tandem with a functionalized-magnetic 3D printer and allows the user to adjust the operating parameters according to the desired DoA.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluation of Spray and Combustion Models for Simulating Dilute Combustion in a Direct-Injection Spark-Ignition Engine

Dilute combustion in spark-ignition engines has the potential to improve thermal efficiency by mitigating knock and by reducing throttling and wall heat losses. However, ignition and combustion processes can become unstable for dilute operation due to a lowered laminar flame speed, resulting in excessive cycle-to-cycle variability (CCV) of the combustion process. To compensate for the slower combustion in less reactive mixtures, a modified intake port geometry can be employed to generate a strong tumble flow in the cylinder and elevate turbulence levels around the spark plug, thereby promoting a faster transition to turbulent deflagration. Consequently, optimizing combustion chamber geometry and operating strategy is crucial to maximizing the benefits of using dilute combustion with enhanced in-cylinder turbulence across a wide range of operating conditions. Computational fluid dynamics (CFD) simulations can be utilized for virtual engine optimization tasks, but this would require the models to be truly predictive regarding the impact of changes to the engine design and operational parameters.In this study, multicycle large-eddy simulations (LES) are performed for a direct-injection spark-ignition engine to investigate the model performance in predicting engine combustion characteristics with respect to changes in the intake configuration. A tumble plate that blocks the lower part of the intake port inlet is used to vary the tumble. A set of CFD models that have been recently developed are employed, which takes into account the drag of nonspherical droplets, flash-boiling behavior of liquid sprays, spray-wall interaction, surrogate formulation of a research-grade E10 gasoline, and fast chemical kinetic solvers. Simulation results are compared to experimental engine data in terms of cylinder pressure, apparent heat release rate, mass fraction burned timing, and flame images. It is found that LES employing the state-of-the-art CFD models are capable of properly predicting the spray processes and reproducing the measured mean cylinder pressure for the case with the tumble plate. On the other hand, the LES over-predicts the combustion rate during the early combustion stage and under-estimates the CCV, and these discrepancies become larger when the tumble plate is removed.

computational fluid dynamics simulation↗