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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Understanding I/O workload characteristics of a Peta-scale storage system

Understanding workload characteristics is critical for optimizing and improving the performance of current systems and software, and architecting new storage systems based on observed workload patterns. In this paper, we characterize the I/O workloads of scientific applications of one of the world's fastest high performance computing (HPC) storage cluster, Spider, at the Oak Ridge Leadership Computing Facility (OLCF). OLCF flagship petascale simulation platform, Titan, and other large HPC clusters, in total over 250 thousands compute cores, depend on Spider for their I/O needs. We characterize the system utilization, the demands of reads and writes, idle time, storage space utilization, and the distribution of read requests to write requests for the Peta-scale Storage Systems. From this study, we develop synthesized workloads, and we show that the read and write I/O bandwidth usage as well as the inter-arrival time of requests can be modeled as a Pareto distribution. We also study the I/O load imbalance problems using I/O performance data collected from the Spider storage system.

I/O↗
DOE OSTI2018

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. Lastly, the aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development of a high current density, high temperature superconducting cable for pulsed magnets

Abstract A low AC loss Rare Earth Barium Copper Oxide (REBCO) cable, based on the VIPER cable technology has been developed by Commonwealth Fusion Systems for use in high field, REBCO based tokamaks. The new cable is composed of partitioned and transposed copper ‘petals’ shaped to fit together in a circular pattern with each petal containing a REBCO tape stack and insulated from each other to reduce AC losses. A stainless steel jacket adds mechanical robustness—also serving as a vessel for solder impregnation—while a tube runs through the middle for cooling purposes. Additionally, fiber optic sensors are placed under the tape stacks for quench detection. To qualify this design, a series of experiments were conducted as part of the SPARC tokamak Central Solenoid Model Coil program—to retire the risks associated with full scale, fast ramping, high flux HTS Central Solenoid (CS) and Poloidal Field (PF) coils for tokamak fusion power plants and net energy demonstrators. These risk study and risk reduction experiments include (1) AC loss measurement and model validation in the range of ~5 T/s, (2) an IxB electromagnetic loading of over 850 kN/m at the cable level and up to 300 kN/m at the stack level, (3) a transverse compression resilience of over 350 MPa, (4) manufacturability at tokamak relevant speeds and scales, (5) cable to cable joint performance, (6) fiber optic based quench detection speed, accuracy, and feasibility, and (7) overall winding pack integration and magnet assembly. The result is a cable technology, now referred to as PIT VIPER, with AC losses that measure fifteen times lower (at ~5 T/s) than its predecessor technology; a 2% or lower degradation of critical current (Ic) at high IxB electromagnetic loads; no detectable Ic degradation up to 570 MPa of transverse compression on the cable unit cell; end to end magnet manufacturing, consistently producing Ic values within 7% of the model prediction; cable to cable joint resistances at 20 K on the order of ~15 nΩ; and fast, functional quench detection capabilities that do not involve voltage taps. This cable technology will be tested comprehensively in a Central Solenoid Model Coil to prove its readiness for compact, high field tokamak operation.

Sanabria, Charlie (ORCID:0000000150175309)↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Constraining the release of Sn to the ambient melting point following shock loading using time-resolved x-ray diffraction

The formation of liquid following release from a shocked state governs the transition from spall to cavitation and the formation of ejecta in metals. In order to build physics-based models of these processes, it is necessary to critically evaluate the relative importance of kinetics and entropy generation during the release along with the accuracy of multiphase equations of state. Tin (Sn) has served as a testbed for a variety of experiments examining strength and ejecta due to its accessible melt boundary and solid–solid phase transitions. This work presents experiments examining the phase evolution of high purity Sn following the shock and release to ambient stress near the melting point. Sn is found to release to states between its ambient solidus and liquidus from approximately 19 to 33 GPa under uniaxial loading, with the two-phase region being characterized by a reduction in the intensity of the (220), (211) β -Sn doublet. Jetting experiments performed at 27–28 GPa exhibit comparable diffraction patterns with what is observed following the uniaxial release. The solid fractions of β-Sn in the ambient mixed phase region are found to decrease linearly with increasing shock stress as increasing liquid Sn is formed. Here, the results provide much needed information for interpreting measurements of dynamic strength at a high strain rate and experiments examining cavitation and shallow bubble collapse in Sn.

, spall↗
DOE OSTI2012

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influencedby weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale iscritical in power system operations because it serves the unit commitment function for slow-starting conventional generators.

17 WIND ENERGY↗
DOE OSTI2026

Understanding Electric Vehicle Range and Charging Needs: Interactions Between Ambient Temperature, Commute Patterns, and State-of-Charge Usage

Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (−18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (−18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Summary Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.

Solaimani, Mohiuddin↗
DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗
DOE OSTI2016

Non-contact magnetically coupled rectilinear-rotary oscillations to exploit low-frequency broadband energy harvesting with frequency up-conversion

Ambient vibrations have a rectilinear and broadband nature and are particularly rich in the low-frequency regions. This letter reports an electromagnetic energy harvester to transform low-frequency broadband rectilinear vibrations into electricity with frequency up-conversion. The harvester consists of a rectilinear oscillator and a rotary oscillator coupled through magnetic force induced by four arc permanent magnets centrosymmetrically distributed on each oscillator. The rotary oscillator also includes two repulsive magnets and six stationary coils with steel screws inside to obtain and maintain four equilibrium positions with shallowed potential wells. The magnetic interaction between the rectilinear oscillator and the rotary oscillator is formulated using a magnetic dipole model. The restoring torque induced by the steel screws on the rotor is experimentally measured. Magnetically coupled governing equations are derived and their numerical solutions are used to characterize the dynamic response of the harvester under chirp excitations. Experimental results demonstrate its excellent harvesting capability of scavenging low-frequency wideband vibrational energy under slow-frequency-drifted excitations, simple harmonic excitations, and mixed-frequency excitations. Under harmonic excitations, the rectilinear oscillator vibrates non-harmonically but approximately periodically, while the rotary counterpart oscillates in a more complex pattern varying with the excitation frequency, which leads to the frequency up-conversion (up to 10 times increase) and broadened bandwidth (25% increase from its resonant frequency). Experiments show an output voltage of 5 V (RMS)/40 V (Peak to Peak) and an output power of 55 mW (RMS)/950 mW (Peak) at an optimal load of 465 Ω under harmonic excitation of 4 Hz at 0.7 g.

Deng, Wei (ORCID:0000000194598151)↗
DOE OSTI2016

Crash energy absorption of two-segment crash box with holes under frontal load

Crash box is one of the passive safety components which designed as an impact energy absorber during collision. Crash box designs have been developed in order to obtain the optimum crashworthiness performance. Circular cross section was first investigated with one segment design, it rather influenced by its length which is being sensitive to the buckling occurrence. In this study, the two-segment crash box design with additional holes is investigated and deformation behavior and crash energy absorption are observed. The crash box modelling is performed by finite element analysis. The crash test components were impactor, crash box, and fixed rigid base. Impactor and the fixed base material are modelled as a rigid, and crash box material as bilinear isotropic hardening. Crash box length of 100 mm and frontal crash velocity of 16 km/jam are selected. Crash box material of Aluminum Alloy is used. Based on simulation results, it can be shown that holes configuration with 2 holes and ¾ length locations have the largest crash energy absorption. This condition associated with deformation pattern, this crash box model produces axisymmetric mode than other models.

42 ENGINEERING↗
DOE OSTI2026

Modeling the Potential Impact of Storage on the US Power Sector in a Multisector Dynamic Context

The electric power sector is expected to grow in size, importance, and complexity around the world as economies expand and electric supply technologies and demand patterns evolve in significant ways. At the same time the electric power sector may see substantial increases in VRE generating technologies which can add variability and uncertainty to the diurnal and seasonal profile of electric supply. With these forces in play, the emergence of modular, flexible electricity storage technologies may have profound impacts on the operation of electric power systems. Here we reduce a storage modeling gap in MSD models by incorporating grid-based electricity storage into the electric sector dynamics of the Global Change Analysis Model-USA (GCAM-USA) with improved power sector representation. We find a potentially significant role for storage technologies in the future of the U.S. power system, with storage capacity ranging from 7.7 to 14.7 GW in 2050 and 13.9 to 31.6 GW in 2100 across several techno-economic scenarios. We also find that storage can help to smooth variability in residual load arising from evolving electricity demands and the introduction of high shares of VRE to the electric grid. This reduces reliance on high-cost peaking generators, improves system-wide capacity factors, and limits curtailment of VRE.

Patel, Pralit L.↗
DOE OSTI2015

The effect of changes in sea surface temperature on linear growth of Porites coral in Ambon Bay

Coral is one of the most important organisms in the coral reef ecosystem. There are several factors affecting coral growth, one of them is changes in sea surface temperature (SST). The purpose of this research is to understand the influence of SST variability on the annual linear growth of Porites coral taken from Ambon Bay. The annual coral linear growth was calculated and compared to the annual SST from the Extended Reconstructed Sea Surface Temperature version 3b (ERSST v3b) model. Coral growth was calculated by using Coral X-radiograph Density System (CoralXDS) software. Coral sample X-radiographs were used as input data. Chronology was developed by calculating the coral’s annual growth bands. A pair of high and low density banding patterns observed in the coral’s X-radiograph represent one year of coral growth. The results of this study shows that Porites coral extents from 2001-2009 and had an average growth rate of 1.46 cm/year. Statistical analysis shows that the annual coral linear growth declined by 0.015 cm/year while the annual SST declined by 0.013°C/year. SST and the annual linear growth of Porites coral in the Ambon Bay is insignificantly correlated with r=0.304 (n=9, p>0.05). This indicates that annual SST variability does not significantly influence the linear growth of Porites coral from Ambon Bay. It is suggested that sedimentation load, salinity, pH or other environmental factors may affect annual linear coral growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Selection of nanobodies with broad neutralizing potential against primary HIV-1 strains using soluble subtype C gp140 envelope trimers

Broadly neutralizing antibodies (bnAbs) against HIV-1 protect from infection and reduce viral load upon therapeutic applications. However no vaccine was able so far to induce bnAbs demanding their expensive biotechnological production. For clinical applications, nanobodies (VHH) derived from heavy chain only antibodies from Camelidae, may be better suited due to their small size, high solubility/stability and extensive homology to human VH3 genes. Here we selected broadly neutralizing nanobodies by phage display after immunization of dromedaries with different soluble trimeric envelope proteins derived from HIV-1 subtype C. We identified 25 distinct VHH families binding trimeric Env, of which 6 neutralized heterologous primary isolates of various HIV-1 subtypes in a standardized in vitro neutralization assay. The complementary neutralization pattern of two selected VHHs in combination covers 19 out of 21 HIV-1 strains from a standardized panel of epidemiologically relevant HIV-1 subtypes. The CD4 binding site was preferentially targeted by the broadly neutralizing VHHs as determined by competition ELISAs and 3D models of VHH-Env complexes derived from negative stain electron microscopy. The nanobodies identified here are excellent candidates for further preclinical/clinical development for prophylactic and therapeutic applications due to their potency and their complementary neutralization patterns covering the majority of epidemiologically relevant HIV-1 subtypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

X-Ray Diffraction Measurements on Laser-Compressed Polycrystalline Samples Using a Short-Pulse Laser Generated X-Ray Source

Existing models for most materials do not describe phase transformations and associated lattice dy- namics (kinetics) under extreme conditions of pressure and temperature. Dynamic x-ray diffraction (DXRD) allows material investigations in situ on an atomic scale due to the correlation between solid-state structures and their associated diffraction patterns. In this LDRD project we have devel- oped a nanosecond laser-compression and picosecond-to-nanosecond x-ray diffraction platform for dynamically-compressed material studies. A new target chamber in the Target Bay in building 983 was commissioned for the ns, kJ Z-Beamlet laser (ZBL) and the 0.1 ns, 250 J Z-Petawatt (ZPW) laser systems, which were used to create 8-16 keV plasma x-ray sources from thin metal foils. The 5 ns, 15 J Chaco laser system was converted to a high-energy laser shock driver to load material samples to GPa stresses. Since laser-to-x-ray energy conversion efficiency above 10 keV is low, we employed polycapillary x-ray lenses for a 100-fold fluence increase compared to a conventional pinhole aperture while simultaneously reducing the background significantly. Polycapillary lenses enabled diffraction measurements up to 16 keV with ZBL as well as diffraction experiments with ZPW. This x-ray diffraction platform supports experiments that are complementary to gas guns and the Z facility due to different strain rates. Ultimately, there is now a foundation to evaluate DXRD techniques and detectors in-house before transferring the technology to Z. This page intentionally left blank.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Crosslinked Polyacrylamide Composite Hydrogels Impregnated with Fly Ash: Synthesis, Characterization and Their Application as Fractures Sealant for High Water Producing Zones in Oil and Gas Wells

Polymeric hydrogels are designed to serve many purposes in various fields of human endeavor. Herein, crosslinked polyacrylamide (PAM) composite hydrogels impregnated with coal fly ash (CFA) were synthesized, characterized and tested as fracture sealing agents in high water producing permeable zones in petroleum industry. The concept of utilizing CFA as an inorganic additive in the matrix of hydrogel emanates from the fact that CFA constitutes majorly alumina and silica. Both chemical oxides can induce the CFA to promote the inherent properties of hydrogel. Polyethyleneimine (PEI) was chosen as the crosslinking agent. Neat PAM/PEI hydrogel and PAM/PEI–CFA hydrogels at various CFA loadings (0.5, 1 and 2 wt%) were synthesized via a transamidation reaction pathway. The developed hydrogels were characterized by hybrid rheometer, FTIR, SEM and XRD instruments. Rheological results reveal that the PAM/PEI–CFA composite hydrogels embedded with various CFA quantities were more elastic than the neat PAM/PEI hydrogel, indicating the dispersion and reinforcing effect of CFA. The functional groups of these hydrogels were confirmed by the FTIR while SEM analysis show that the surface micrographs of neat PAM/PEI hydrogel and PAM/PEI–CFA1 hydrogel were porous in several regions. In contrast, the micrographs of PAM/PEI–CFA2 and PAM/PEI–CFA3 hydrogels demonstrated dense and “net-like” structural patterns. Further, XRD analysis revealed that CFA impregnation has a significant effect on the bulk structural properties of the fabricated hydrogels. The swelling rates of these hydrogels were determined by the gravimetric method and their diffusion parameters evaluated using Fickian diffusion and Schott-order kinetic models. Efficacy of the PAM/PEI–CFA composite hydrogel as fractures sealant in oil and gas wells was conducted at a typical reservoir temperature, 90 °C, and the outcome demonstrated considerable sealing potency.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Characterization and Integrated Fabrication of Al Components with Thick TiC{sub p}/Al Composite Coatings via Self-Propagating Reaction

A novel and quick process combining self-propagating reaction with vacuum-expendable pattern casting is presented to accomplish integrated preparation of millimeter-thick TiC{sub p}/Al composite coatings on the Al substrate. Titanium and graphite powders were mixed with the polytetrafluoroethylene (PTFE) additive and pasted on the expendable polystyrene (EPS) followed by compaction. Al components with in situ TiC{sub p}/Al composite coating were simultaneously synthesized at a relatively low temperature of molten Al, which was poured into the assembly of EPS and powder compact inside the silica sands. Differential scanning calorimetry, x-ray diffraction, scanning electron microscopy with energy-dispersive spectroscopy and transmission electron microscopy were used to systematically investigate the thermodynamic characteristics of the reaction, the phase constituents, microstructure and tribological properties of the coatings. The infiltration model describing the densification of the reaction coatings was also developed for the critical wetting condition. The wear characteristics were assessed using a pin-on-disk tribometer with various loads. Results showed that uniform TiC particles stimulated by the PTFE-activated self-propagating reaction afforded superior mechanical properties and wear resistance.

36 MATERIALS SCIENCE↗