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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 361 records · Page 20
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DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗
DOE OSTI2026

Power Electronic Development Challenges for High Power Density Integrated Electric Drive Applications

The ever-increasing demand for compact, efficient, and high-performance traction drive systems for transportation applications has accelerated the development of integrated electric drives. In these systems, the electric machine and inverter are integrated within a single housing, offering significant advantages in electrical performance, volume, weight, cost, and overall system efficiency. Despite these benefits, such high levels of integration introduce a new set of challenges, particularly in power electronic design and component selection. This article presents an in-depth investigation of a highly integrated electric drive architecture with an internal stator-mounted inverter, highlighting key design considerations and trade-offs aimed exclusively at maximizing system power density.Based on a comprehensive review of the literature, the power density of a voltage-source-inverter-driven electric drive is primarily governed by the volumetric contributions of the power modules, heat sinks, and DC-link capacitors. Accordingly, this work focuses on the optimization of these three critical components to enhance the inverter's overall power density. The proposed design achieves a power density of 100 kW/L, demonstrating the effectiveness of the presented approach for next-generation integrated electric drive systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Online Learning of Effective Turbine Wind Speed in Wind Farms

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor-averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.

Gaussian process↗
DOE OSTI2020

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

batch job preemption↗
DOE OSTI2024

Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing

A coloring of a graph is an assignment of colors to vertices such that no two neighboring vertices have the same color. The need for memory-efficient coloring algorithms is motivated by their application in computing clique partitions of graphs arising in quantum computations where the objective is to map a large set of Pauli strings into a compact set of unitaries. We present Picasso, a randomized memory-efficient iterative parallel graph coloring algorithm with theoretical sublinear space guarantees under practical assumptions. The parameters of our algorithm provide a trade-off between coloring quality and resource consumption. To assist the user, we also propose a machine learning model to predict the coloring algorithm’s parameters considering these trade-offs. We provide a sequential and a parallel implementation of the proposed algorithm. We perform an experimental evaluation on a 64-core AMD CPU equipped with 512 GB of memory and an Nvidia A100 GPU with 40GB of memory. For a small dataset where existing coloring algorithms can be executed within the 512 GB memory budget, we show up to 68× memory savings. On massive datasets we demonstrate that GPU-accelerated Picasso can process inputs with 49.5× more Pauli strings (vertex set in our graph) and 2,478× more edges than state-of-the-art parallel approaches.

artificial intelligence, quantum computing↗
DOE OSTI2026

Multi-Objective Boundary Analysis of Discrete and Integrated SiC FET Modular Non-inverting Buck and Boost Converters for Fuel Cell EVs

This paper presents a multi-objective analysis of discrete and integrated SiC FET-based non-inverting buck-boost converter modules for modular fuel cell electric vehicle (EV) systems. Two converter ratings, 60 kW and 90 kW, are evaluated for both implementations, scalable up to 420 kW and 450 kW, respectively. Performance is assessed across efficiency, volumetric and gravimetric power density, cost, thermal stress, and estimated lifetime, where lifetime is derived from SiC FET B10 power-cycling data and junction temperature variations at rated power. A normalized overall performance index combined with a Pareto-boundary framework is used to identify configurations that optimally balance competing objectives. Results show that most configurations lie on the Pareto front, providing balanced trade-offs, while certain high-power discrete (90 kW at 450 kW) and integrated (60 kW at 180−420 kW) configurations are dominated. In general, discrete modules are more favorable for lower-power modular systems due to higher power density and lower cost, whereas integrated modules become more advantageous at higher power levels due to improved thermal behavior and longer lifetime. These findings provide practical design guidance for scalable fuel cell converter architectures and highlight the importance of system-level trade-offs in modular power electronics design.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2022

Evaluating Methods for Measuring Grid Frequency in Low-Inertia Power Systems: Preprint

Accurate measurement of grid frequency is a critical component of reliable grid control. Traditionally, methods such as phase locked loops (PLLs) and discrete Fourier transforms (DFTs) have been used in inverters and phasor measurement units (PMUs) to measure frequency. However, as the percentage of inverter-based resources (IBRs) such as solar and wind has increased, these conventional frequency measurement methods are proving unable to guarantee reliable control in some cases. One challenge is measuring frequency during transient events, where there is a disruption in the steady state sinusoidal voltage. During these events, the underlying frequency of the grid may barely change, but measurement methods report a large spike in frequency due to the disrupted waveform. New methods must balance between suppressing spikes in frequency during faults, and providing fast, accurate, measurements in all other grid operation conditions, especially during events with high rate-of-change-of frequency (ROCOF), which are more prevalent in high-IBR power systems. This paper first surveys frequency measurement methods that have been proposed to reduce measurement errors during transient events. Then, both conventional and more novel frequency measurement methods are tested against an IEEE standard and industry recommendations, and their performance is evaluated for events simulated in PSCAD. Results quantify the trade-offs in performance during different grid conditions and lead to suggestions for the most appropriate frequency and ROCOF measurement methods for low inertia grids.

frequency↗
DOE OSTI2023

Transactive Emergency Power Allocation

The devastating impacts of extreme weather events, many of which are climate-change related, are increasingly evident through the frequency and duration of outages on power grids, especially the distribution systems. One of the major modern-day concerns of utilities is dealing with such extreme supply outages where operators have used rolling blackouts as a contingency plan balance supply and demand while serving critical loads. Such events have significant repercussions social and economic costs. Such blackouts practices are a blunt instrument, depriving customers with low-capacity high-priority loads (i.e. refrigeration, water, telecommunication, etc.) that provide high marginal amenity. Our contribution presented in this work is a transactive emergency allocation mechanism that would provide some minimum level of service to all of the customers while enabling preference-based trading of this initial allocation. This is in contrast to the state-of-art TE mechanisms that allocate resources to customers solely based on their willingness-to-pay. The effectiveness of the proposed mechanism is demonstrated through simulation-based evaluation on a prototypical distribution system experiencing 12-hour scarcity-based emergency event due to extreme operating conditions. Simulation results clearly demonstrate the capability of the proposed transactive emergency allocation mechanism in effectively utilizing the available energy and providing some level of service to all customers to operate their high-priority loads throughout the extreme scarcity event while being economically efficient in allowing trading of allocation based on customer preferences.

transactive energy, power system economics, emerge↗
DOE OSTI2022

Understanding the Impact of Data Staging for Coupled Scientific Workflows

We report the rate of data generated by cutting-edge experimental science facilities and large-scale simulations enabled by current high-performance computing (HPC) systems has continued to grow at a far greater pace than the development of the network and storage capabilities on which these systems rely. To cope with this challenge, scientist are moving toward the creation of autonomous experiments and HPC simulations using machine learning. However, efficiently moving, storing, and processing large amounts of data away from the point of origin presents an incredible challenge. In-memory computing, in situ analysis, data staging, and data streaming are recognized viable alternatives to traditional file-based methods for transferring data between coupled workflows. However, the performance trade-offs and limitations for these methods are not fully understood when used in HPC applications. This article presents a comprehensive performance assessment of the current solutions for data staging when applied to applications that are not necessary I/O intensive which makes them not ideal candidates for these methods. Our study is based on experiments running at scale on Oak Ridge National Laboratory's Summit supercomputer using applications and simulations that cover typical computational motifs and patterns. We investigated the usability and cost/benefit trade-offs of staging algorithms for HPC applications under different scenarios and highlight opportunities for optimizing the dataflow between coupled simulation workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Selling Demand Response Using Options

Wholesale electricity markets in many jurisdictions use a two-settlement structure: a day-ahead market for bulk power transactions and a real-time market for fine-grain supply-demand balancing. This paper explores trading demand response assets within this two-settlement market structure. Here, we consider two approaches for trading demand response assets: (a) an intermediate spot market with contingent pricing, and (b) an over-the-counter options contract. In the first case, we characterize the competitive equilibrium of the spot market, and show that it is socially optimal. Economic orthodoxy advocates spot markets, but these require expensive infrastructure and regulatory blessing. In the second case, we characterize competitive equilibria and compare its efficiency with the idealized spot market. Options contract are private bilateral over-the-counter transactions and do not require regulatory approval. We show that the optimal social welfare is, in general, not supported. We then design optimal option prices that minimize the social welfare gap. This optimal design serves to approximate the ideal spot market for demand response using options with modest loss of efficiency. Our results are validated through numerical simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems

Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. Here, to achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.

Situational awareness↗
DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization

Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups can present a design pitfall if not disambiguated, they also present a design opportunity—namely, the development of visualizations that support multiple groups. This development choice presents a trade-off of broadening the scope but limiting support for the more narrow tasks of any one group, which in some cases can enhance the overall utility of the visualization. We investigate this scenario through a design study where we develop Guidepost, a notebook-embedded visualization of data that helps scientists assess compute wait times, machine learning researchers understand prediction accuracy, and system maintainers analyze usage trends. We adapt the use of personas for visualization design from existing literature in the HCI and design domains, applying them to categorize tasks based on their uniqueness across stakeholder personas. Under this model, tasks shared between all groups should be supported by interactive visualizations and tasks unique to each group can be deferred to scripting with notebook-embedded visualization design. We evaluate our visualization through real-world case studies and a task-focused evaluation with nine participants. We observe that together, Guidepost's visual encodings, interactions, and export capabilities support the tasks of our differing personas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The impact of interventions in the global land and agri-food sectors on Nature’s Contributions to People and the UN Sustainable Development Goals

Interlocked challenges of climate change, biodiversity loss and land degradation require transformative interventions in the land management and food production sectors to reduce carbon emissions, strengthen adaptive capacity, and maintain or increase food production to 2050. However, deciding which interventions to pursue and understanding their relative synergies with and trade-offs against different social and environmental goals has been difficult without direct comparisons across a range of possible actions. This study examined a series of 40 different options, implemented through land, value chain or risk management, for their relative impacts across 18 Nature’s Contributions to People (also known as ecosystem services) and the 17 Sustainable Development Goals. We find that a relatively small number of interventions show significant positive synergies with both SDGs and NCPs; these include improved cropland management, improved grazing land management, improved livestock management, agroforestry, agricultural diversification, increased soil organic carbon content, reduced soil erosion, salinization and compaction, fire management, reduced landslides and hazards, reduced pollution, management of invasive species, sustainable source, and reduced post-harvest losses. Several interventions show strong negative impacts on either SDGs, NCPs or in some cases, both; these include bioenergy and BECCS, afforestation, and some risk sharing measures, like commercial crop insurance. Our results demonstrate that a better understanding of benefits and trade-offs of comparative policy approaches can help decision-makers choose the most effective, or at the very minimum, benign interventions for implementation in specific contexts.

Mcelwee, Pam↗
DOE OSTI2025

Switchgrass Steroidal Saponins Reduce Fungal Disease but Decrease Yeast Fermentation Yield

Increasing the production of bioproducts from lignocellulosic feedstocks requires improvement in both field production and biorefinery efficiency. When plant traits arise that improve field production but decrease biofuel yield, these trade-offs can represent challenges in the entire production process. To examine trade-offs between field and production traits, we examined factors underlying switchgrass resistance to fungal rust pathogens in field conditions and factors that impede yeast fermentation in the lab using repeated measurements on a switchgrass genetic diversity panel. We found that the same switchgrass genotypes that showed high fungal pathogen resistance also showed recalcitrance to yeast fermentation. These switchgrass genotypes were mostly from the Atlantic genetic group, which had high levels of specialized metabolites of the saponin class. Among 1589 metabolites identified through metabolomics, we found that saponins were among the most likely to explain variation in both rust infection and fermentation yield using random forest feature selection, and that only four of these were sufficient to explain 57.9% of the variation in rust susceptibility. Through follow-up testing in recalcitrant biomass, we found that the bacterium Zymomonas mobilis does not suffer the same inhibition as the yeast Saccharomyces cerevisiae, and that the addition of ergosterol (thought to be the fungal cellular target of saponin inhibition) rescues yeast fermentation. Several lines of evidence point to a central role for saponins as key metabolites protecting switchgrass from fungal pathogens and interfering with yeast fermentation, underscoring an ongoing need for collaboration between plant breeders and biofuel production scientists.

VanWallendael, Acer [North Carolina State Universi↗
DOE OSTI2021

Beyond leaf habit: generalities in plant function across 97 tropical dry forest tree species

Leaf habit has been hypothesized to define a linkage between the slow-fast plant economic spectrum and the drought resistance-avoidance trade-off in tropical forests (‘slow-safe vs fast-risky’). However, variation in hydraulic traits as a function of leaf habit has rarely been explored for a large number of species. We sampled leaf and branch functional traits of 97 tropical dry forest tree species from four sites to investigate whether patterns of trait variation varied consistently in relation to leaf habit along the ‘slow-safe vs fast-risky’ trade-off. Leaf habit explained from 0% to 43.69% of individual trait variation. We found that evergreen and semi-deciduous species differed in their location along the multivariate trait ordination when compared to deciduous species. While deciduous species showed consistent trait values, evergreen species trait values varied as a function of the site. Last, trait values varied in relation to the proportion of deciduous species in the plant community. Here We found that leaf habit describes the strategies that define drought avoidance and plant economics in tropical trees. However, leaf habit alone does not explain patterns of trait variation, which suggests quantifying site-specific or species-specific uncertainty in trait variation as the way forward.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Using leaf and stomatal traits to predict biomass production and water use efficiency in Populus

Climate change is reshaping ecosystems, driving plants to adapt through leaf-trait plasticity that reflects strategies for growth and water use. Predicting biomass production and intrinsic water use efficiency (iWUE) remains challenging because of genetic, taxonomic, and environmental variability. Here, we used eastern cottonwood and Populus hybrids as a model system to test whether easily measurable leaf traits can serve as reliable predictors of performance, and whether adding stomatal and biochemical traits improves predictive power. Across two field sites in Mississippi, leaf mass per area (LMA), biomass production, iWUE, leaf area, and foliar nitrogen ( N %) differed significantly among taxa and sites, while other traits were conserved. Factorial analysis of mixed data (FAMD) revealed distinct clustering of taxa and sites, indicating coordinated variation among leaf and stomatal traits. Pairwise correlations highlighted fundamental trade-offs, with biomass positively related to LMA and petiole length but negatively associated with iWUE, N %, and carbon isotopic ratios (δ 13 C). Leaf temperature and leaf angle varied among taxa and were significantly correlated with LMA and petiole length, suggesting mechanisms of heat dissipation and leaf movability that link simple traits to gas exchange and productivity. Weighted multiple linear regression models explained 80%–91% of variation in biomass production and iWUE. Models using only LMA, petiole length, and stomatal metrics performed nearly as well as those incorporating N %, and δ 13 C, with complex traits adding approximately 10% explanatory power. These results demonstrate that simple morphological traits capture integrated functional trade-offs, while complex traits refine predictions. This tiered approach provides an efficient framework for selecting high-yielding, water-efficient genotypes of Populus and other hardwood species, offering practical pathways to enhance carbon uptake and iWUE under climate change.

biomass production↗
DOE OSTI2023

Electric and Hydraulic Propel Torque Modulation for a Compact Track Loader With the Hybrid Hydraulic Electric Architecture (HHEA)

Abstract The Hybrid Hydraulic Electric Architecture (HHEA) has previously been proposed for off-highway vehicles to reap the efficiency and controllability benefits of electrification without needing very large electric motors. This is achieved with the use of a set of selectable common pressure rails to transmit the majority of power and small electric motors to modulate that power. Previous work has shown significant energy savings for the work circuits of a variety of machines. In this paper, the energy saving potential of HHEA for the propel circuit of a compact track loader is studied. The ports of the track hydraulic motors are selectably connected to the common pressure rails, and instead of using the electric assist motors to buck/boost pressure, as in HHEA for linear actuators, small electric assist motors are used to add/subtract torque directly. The interplay between the torque limits of the electric motors and the ability of the hydraulic motor to vary displacements is studied, along with the effect these factors have on energy saving potential. It is found that the ability to vary the displacement of the hydraulic motor allows for: more efficient electric motor operating conditions, reduced electric torque requirement, and reduced pressure rail switching events. All three of these advantages can be achieved at once using variable displacements; but trade-offs exist between these advantages (i.e. improved efficiency can be achieved at the expense of a larger electric torque requirement). Overall, the HHEA can reduce energy consumption by ∼ 36% compared to the stock machine, depending on the hydraulic motor’s ability to vary displacements, and assuming the electric motor torque is limited to 20% of that required in a direct electrification scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗