Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Generators”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Grid Value Propositions for Tidal Generation Resources - a Temporal Analysis

As the energy sector experiences a paradigm shift towards cleaner and greener generation sources, the need for evaluating the applicability of marine energy for grid-based applications has intensified. This is because marine energy (ocean, wave and tidal) may be utilized to provide unique benefits to electric grid operations, especially in conjunction with other established renewable technologies (such as wind and solar) or technologies like energy storage. In this work, we focus our attention on some specific characteristics of two types of marine energy resources i.e. tidal and ocean current based resources. Specifically, we perform a comprehensive temporal analysis using a suite of metrics on resource data to characterize the inherent predictability of the resource across various time scales of interest. We also investigate the correlation of tidal power generation profiles with energy demand and comment on the effectiveness of having such resources in the overall generation portfolio.

Bhattacharya, Saptarshi↗
DOE OSTI2024

Evaluating the Impact of Retiring Synchronous Fossil Fuel Generators on Inter-Area Oscillations in the U.S. Western Interconnection

To facilitate the decarbonization of the power grid, fossil-fuel-based synchronous generators are gradually being retired and replaced by inverter-interfaced renewable energy resources. As synchronous machines get displaced, the characteristics of inter-area oscillation modes in electrical interconnections will change. This paper describes a model-based study of the impact of the retirement of synchronous fossil-fuel generators on the oscillatory characteristics of the US Western Interconnection (WI). Results show that if fossil-fuel-based synchronous machines are replaced by grid-forming inverters, then dominant inter-area modes seen in the WI today will change. New inter-area modes may appear due to the electromechanical energy exchange among remaining hydropower generators clustered in geographical proximity in the northwestern part of the WI. This observation indicates that the evolution in modal properties needs to be closely tracked as the resource mix in electrical interconnections changes.

grid forming inverter, wide area oscillation, Inte↗
DOE OSTI2026

Alaska Observed Hydropower Generation

This dataset contains compiled observed hydropower generation for hydropower plants in Alaska. Data have been compiled from data provided to the Energy Information Administration by asset owners, data contained in annual reports produced by the Institute of Social and Economic Research at the University of Alaska Anchorage (Alaska Electric Power Statistics and Alaska Energy Statistics) and data provided to the Federal Energy Regulatory Commission by asset owners. This dataset provides available generation data from all sources in monthly and annual files, with quality flags, and generation data identifying the highest quality source in monthly and annual files.

Broman, Daniel [Pacific Northwest National Laborat↗
DOE OSTI2023

Posiform planting: generating QUBO instances for benchmarking

We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. Such problems are NP-hard in general, implying that the exact minima of randomly generated instances are hard to find and thus typically unknown. While brute forcing smaller instances is possible, such instances are typically not interesting due to being too easy for both quantum and classical algorithms. In this contribution, we propose a novel method, called posiform planting , for generating random QUBO instances of arbitrary size with known optimal solutions, and use those instances to benchmark the sampling quality of four D-Wave quantum annealers utilizing different interconnection structures (Chimera, Pegasus, and Zephyr hardware graphs) and the simulated annealing algorithm. Posiform planting differs from many existing methods in two key ways. It ensures the uniqueness of the planted optimal solution, thus avoiding groundstate degeneracy, and it enables the generation of QUBOs that are tailored to a given hardware connectivity structure, provided that the connectivity is not too sparse. Posiform planted QUBOs are a type of 2-SAT boolean satisfiability combinatorial optimization problems. Our experiments demonstrate the capability of the D-Wave quantum annealers to sample the optimal planted solution of combinatorial optimization problems with up to 5, 627 qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combined Sugarcane Pretreatment for the Generation of Ethanol and Value-Added Products

In this work, we have tested individual and combination of applications of ozonolysis and liquid hot water (LHW) to pretreat sugarcane bagasse (SCB) for the removal of enzyme and/or microbial inhibitors and generation of potential value-added chemicals. A solid content with 80% cellulose and a liquid phase (liquor) rich in phenolic derived compounds (3 g . L −1 ) from lignin, sugars (>20 g . L −1 ), and other compounds, such as furfural and hydroxymethylfurfural (HMF), were generated. Maximal (59%) glucan conversion occurred in the presence of double-pretreated bagasse, which had 32–50% more glucan available than the samples that were individually LHW or ozone-pretreated, resulting in maximal ethanol production (92% after 42 h) from double-pretreated SCB enzyme hydrolyzate. In summary, this work showed that ozone reacts effectively with lignin without the use of any other chemical reagent, and LHW pretreatment, followed by a washing step, was effective in solubilizing and cleaning up the fiber enzyme and microbial inhibitory compounds with ozone being effective against phenolics. Moreover, the generated cellulose-rich substrate is readily fermentable. The acidic liquor fraction removed by sequential washings and containing mainly sugars and phenolic compounds may be evaluated for use in green chemistry bioconversions processes.

09 BIOMASS FUELS↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2025

Leveraging CRISPR Cas9 RNPs and Cre- loxP in Picochlorum celeri for generation of field deployable strains and selection marker recycling

As new highly productive strains of algae are discovered and developed to meet the energy, chemical, and food requirements of the future, genetic engineering of those strains in a manner that yields deployable transformants is paramount. This study introduces the novel CRoxP ($\underline{\textrm{C}}$$\textrm{as9}$ $\underline{\textrm{R}}$$\textrm{NPs}$ coupled with an inducible $\underline{\textrm{CR}}$$\textrm{e}$-$\textrm{l}\underline{\textrm{oxP}}$) system for rapid generation of marker- and transgene-free strains of Picochlorum celeri. The CRoxP system allows reuse of selection markers without Cas9 expression in vivo, eliminating many of the bottlenecks associated with conventional CRISPR Cas9 use for precise genome editing. In P. celeri, transformants were generated with a turnaround time as short as 21 days between transformation and being ready for another round of transformation with the same selection marker by using the CRoxP system. As a use-case for CRoxP, depigmented strains of P. celeri were generated by multiplexed Cas9 disruption of major LHCII genes followed by either a second round of LHCII targeting, or knockout of an LHCI gene. One transformant tested in flask culture (R6) exhibited similar biomass production to the wild type with 46% less Chl a + b on a biomass basis. In photobioreactors and under diel light simulating a solar day, a transformant (LhcBM31) exhibited 34 g AFDW m –2 d –1 with 54% less Chl a + b on a biomass basis vs. wild type.

09 BIOMASS FUELS↗
DOE OSTI2022

A computational pipeline to generate a synthetic dataset of metal ion sorption to oxides for AI/ML exploration

The charged mineral/electrolyte interfaces are ubiquitous in the surface and subsurface–including the surroundings of the geological disposal sites for radioactive waste. Therefore, understanding how ions interact with charged surfaces is critically important for predicting radionuclide mobility in the case of waste leakage. At present, the Surface Complexation Models (SCMs) are the most successful thermodynamic frameworks to describe ion retention by mineral surfaces. SCMs are interfacial speciation models that account for the effect of the electric field generated by charged surfaces on sorption equilibria. These models have been successfully used to analyze and interpret a broad range of experimental observations including potentiometric and electrokinetic titrations or spectroscopy. Unfortunately, many of the current procedures to solve and fit SCM to experimental data are not optimal, which leads to a non-transferable or non-unique description of interfacial electrostatics and consequently of the strength and extent of ion retention by mineral surfaces. Recent developments in Artificial Intelligence (AI) offer a new avenue to replace SCM solvers and fitting algorithms with trained AI surrogates. Unfortunately, there is a lack of a standardized dataset covering a wide range of SCM parameter values available for AI exploration and training–a gap filled by this study. Here, we described the computational pipeline to generate synthetic SCM data and discussed approaches to transform this dataset into AI-learnable input. First, we used this pipeline to generate a synthetic dataset of electrostatic properties for a broad range of the prototypical oxide/electrolyte interfaces. The next step is to extend this dataset to include complex radionuclide sorption and complexation, and finally, to provide trained AI architectures able to infer SCMs parameter values rapidly from experimental data. Here, we illustrated the AI-surrogate development using the ensemble learning algorithms, such as Random Forest and Gradient Boosting. These surrogate models allow a rapid prediction of the SCM model parameters, do not rely on an initial guess, and guarantee convergence in all cases.

Li, Chunhui↗
DOE OSTI2025

High-Voltage Pulsed Power Generator for Beam Injection Systems

Beam injection systems in hadron colliders require kickers generating ±50 kV peak voltages into a 50 Ω impedance, with peak currents of 1000 A and sub-10 ns rise and fall times. This paper presents a novel high-voltage pulse power generator utilizing a distributed pulser architecture. It combines gallium nitride (GaN) transistors in a Marx topology with an inductive adder, achieving nanosecond-scale switching speeds and high-power efficiency. Compared to other solutions such as based on MOSFETs or fast ionization dynistors, our development offers superior peak and average power performance, reduced system complexity, and enhanced reliability, marking a significant step forward in high-voltage pulse generation for accelerator applications.

Smirnov, Alexander (ORCID:0000000280631691)↗
DOE OSTI2021

Grid-Scale Ternary-Pumped Thermal Electricity Storage for Flexible Operation of Nuclear Power Generation under High Penetration of Renewable Energy Sources

In this work, the integration of a grid-scale ternary-Pumped Thermal Electricity Storage (t-PTES) with a nuclear power generation to enhance operation flexibility is assessed using physics-based models and digital real time simulation. A part of the electricity from the nuclear power generation is delivered to the grid, and the balance is used to power a heat pump that can be augmented by an auxiliary resistive load element to increase the charging rate of the thermal storage. This increases the thermal potential between hot and cold thermal stores (usually solid materials or molten salts inside large storage tanks). The thermal energy is transformed back into electricity by reversing the heat pump cycle. Different transient scenarios including startup, shutdown, and power change for grid-connected operation are simulated to determine the behavior of the hybrid nuclear-t-PTES system operating under variable loads that constitute a departure from conventional, baseload nuclear plant operation schemes. Ternary refers to the three modes operation: (i) heat pump (including heating coil), (ii) heat engine, and (iii) simultaneous operation of heat pump (including heating coil) and heat engine during changeover from pumping to generation or vice-versa. The controllability of t-PTES in the short timescales as a dynamic load is used to demonstrate operational flexibility of hybrid nuclear plants for flexible operation through advanced load management. The integration of t-PTES into nuclear power systems enhances the system flexibility and is an enabler for high penetration of renewable energy resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Voltage Service Limits Smart Contract Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Generator

Modern electrical grids face growing stability risks from customer-owned generators, especially at points of common couplings (PCCs). Disruptive behavior from power-electronic sources can cause protective relays to isolate problematic generators, making measurement integrity critical. This article presents a distributed ledger technology (DLT) approach that uses smart contracts to evaluate PCC voltage measurements and trigger backup breaker operations. The approach is framed as a verifiable, multi-organization attestation and audit layer, not as a real-time control security mechanism. In the proposed architecture, voltage measurements from a hardware protective relay are anchored on a DLT through the Cyber Grid Guard (CGG) system for attestation by both the grid utility and customer-owned generator. A Voltage Service Limits (VSLs) smart contract evaluates the on-chain measurements against allowable phase-voltage limits derived from the ANSI C84.1 standard. The framework is validated in a hardware-relay-in-the-loop test bed under sustained-undervoltage, sustained-overvoltage, and transient line-to-line fault scenarios. The results show that the VSL smart contract can process these measurements and issue backup breaker actions consistent with the defined service-limit criteria, demonstrating the DLT potential as a verifiable audit layer at the PCC that complements primary protection.

cyber security↗
DOE OSTI2024

A Generative Model for Realistic Galaxy Cluster X-Ray Morphologies

Abstract The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or “relaxed.” Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from megaparsecs down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from The Three Hundred project to define a basis set of cluster image features. We take advantage of the clusters’ approximate self-similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high-order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms and could be straightforwardly adapted to generate clusters displaying specific types of features or selected by physical characteristics available in the original simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Electron-scale Magnetic Holes Generation Driven by Whistler-to-Bernstein Mode Conversion in Fully Kinetic Plasma Turbulence

Magnetic holes (MHs) are coherent structures characterized by a strong and localized magnetic field amplitude dip, commonly observed in the heliosphere. These structures come in different sizes, from magnetohydrodynamic to kinetic scales. Subion-scale MHs are usually sustained by an electron current vortex and exhibit a strong electron temperature anisotropy, with higher temperatures perpendicular to the background magnetic field. Magnetospheric multiscale observations (MMSs) have revealed electron-scale MHs to be ubiquitous in the turbulent Earth’s magnetosheath and the solar wind, potentially playing an important role in the energy cascade and dissipation. Despite abundant observations, the origin of electron-scale MHs is still unclear and debated. In this work, we use fully kinetic simulations to investigate the role of plasma turbulence in generating electron-scale MHs. We find that the turbulence spontaneously produces electron-scale MHs via the following mechanism: first, large-scale turbulent velocity shears produce regions with high electron temperature anisotropy; these localized regions become unstable, generating oblique electron-scale whistler waves; as they propagate over the inhomogeneous turbulent background, whistler fluctuations develop an electrostatic component, turning into Bernstein-like modes; the strong electrostatic fluctuations produce current filaments that merge into an electron-scale current vortex; the resulting electron vortex locally reduces the magnetic field amplitude, finally evolving into an electron-scale MH. We show that MHs generated by this mechanism have properties consistent with MMSs and nontrivial kinetic features with a “mushroom”-shaped electron velocity distribution function. Our results have potential implications for understanding the formation and occurrence of electron-scale MHs in astrophysical turbulent and space environments, such as the Earth’s magnetosheath and the solar wind.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Deterministic Multivariate Clustering Method for Drive Cycle Generation from In-Use Vehicle Data

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data. This paper explores the development and initial validation of a method of generating representative drive cycles from large collections of real-world vehicle data using a deterministic multivariate clustering approach. Starting with theory and diving into the methodology behind representative cycle generation, the paper aims to also present graphical and tabular results of initial validation via vehicle simulation and chassis dynamometer testing. Additional topics for further research and areas for ongoing development will also be presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Comparing different generations of idealized solar geoengineering simulations in the Geoengineering Model Intercomparison Project (GeoMIP)

Abstract. Solar geoengineering has been receiving increased attention in recent years as a potential temporary solution to offset global warming. One method of approximating global-scale solar geoengineering in climate models is via solar reduction experiments. Two generations of models in the Geoengineering Model Intercomparison Project (GeoMIP) have now simulated offsetting a quadrupling of the CO2 concentration with solar reduction. This simulation is idealized and designed to elicit large responses in the models. Here, we show that energetics, temperature, and hydrological cycle changes in this experiment are statistically indistinguishable between the two ensembles. Of the variables analyzed here, the only major differences involve highly parameterized and uncertain processes, such as cloud forcing or terrestrial net primary productivity. We conclude that despite numerous structural differences and uncertainties in models over the past two generations of models, including an increase in climate sensitivity in the latest generation of models, the models are consistent in their aggregate climate response to global solar dimming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A thermal-driven graupel generation process to explain dry-season convective vigor over the Amazon

Large-eddy simulations (LESs) are conducted for each day of the intensive observation periods (IOPs) of the Green Ocean Amazon (GoAmazon) field campaign to characterize the updrafts and microphysics within deep convective cores while contrasting those properties between Amazon wet and dry seasons. Mean Doppler velocity (V dop ) values simulated using LESs are compared with 2-year measurements from a radar wind profiler (RWP) as viewed by statistical composites separated according to wet- and dry-season conditions. In the observed RWP and simulated LES V dop composites, we find more intense low-level updraft velocity, vigorous graupel generation, and intense surface rain during the dry periods compared with the wet periods. To investigate coupled updraft–microphysical processes further, single-day golden cases are selected from the wet and dry periods to conduct detailed cumulus thermal tracking analysis. Tracking analysis reveals that simulated dry-season environments generate more droplet-loaded low-level thermals than wet-season environments. This tendency correlates with seasonal contrasts in buoyancy and vertical moisture advection profiles in large-scale forcing. Employing a normalized time series of mean thermal microphysics, the simulated cumulus thermals appear to be the primary generator of cloud droplets. When subsequent thermals penetrate the ice crystal layer, droplets within the thermals interact with entrained ice crystals, which enhances riming in the thermals. This appears to be a production pathway of graupel/hail particles within simulated deep convective cores. In addition, less-diluted dry-case thermals tend to be elevated higher, and graupel grows further during sedimentation after spilling out from thermals. Therefore, greater concentrations of low-level moist thermals likely result in more graupel/hail production and associated dry-season convective vigor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Emulating aerosol optics with randomly generated neural networks

Abstract. Atmospheric aerosols have a substantial impact on climate and remain one of the largest sources of uncertainty in climate prediction. Accurate representation of their direct radiative effects is a crucial component of modern climate models. However, direct computation of the radiative properties of aerosol populations is far too computationally expensive to perform in a climate model, so optical properties are typically approximated using a parameterization. This work develops artificial neural networks (ANNs) capable of replacing the current aerosol optics parameterization used in the Energy Exascale Earth System Model (E3SM). A large training dataset is generated by using Mie code to directly compute the optical properties of a range of atmospheric aerosol populations given a large variety of particle sizes, wavelengths, and refractive indices. Optimal neural architectures for shortwave and longwave bands are identified by evaluating ANNs with randomly generated wirings. Randomly generated deep ANNs are able to outperform conventional multilayer-perceptron-style architectures with comparable parameter counts. Finally, the ANN-based parameterization produces significantly more accurate bulk aerosol optical properties than the current parameterization when compared with direct Mie calculations using mean absolute error. The success of this approach makes possible the future inclusion of much more sophisticated representations of aerosol optics in climate models that cannot be captured by extension of the existing parameterization scheme and also demonstrates the potential of random-wiring-based neural architecture search in future applications in the Earth sciences.

54 ENVIRONMENTAL SCIENCES↗