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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Application of Jacobian-free Newton–Krylov method in implicitly solving two-fluid six-equation two-phase flow problems: Implementation, validation and benchmark

This work represents a first-of-its-kind successful application to employ advanced numerical methods in solving realistic two-phase flow problems with two-fluid six-equation two-phase flow model. These advanced numerical methods include high-resolution spatial discretization scheme with staggered grids (high-order) fully implicit time integration schemes, and Jacobian-free Newton–Krylov (JFNK) method as the nonlinear solver. The computer code developed in this work has been extensively validated with existing experimental flow boiling data in vertical pipes and rod bundles, which cover wide ranges of experimental conditions, such as pressure, inlet mass flux, wall heat flux and exit void fraction. Additional code-to-code benchmark with the RELAP5-3D code further verifies the correct code implementation. The combined methods employed in this work exhibit strong robustness in solving two-phase flow problems even when phase appearance (boiling) and realistic discrete flow regimes are considered. Transitional flow regimes used in existing system analysis codes, normally introduced to overcome numerical difficulty, were completely removed in this work. As a result, this in turn provides the possibility to utilize more sophisticated flow regime maps in the future to further improve simulation accuracy.

42 ENGINEERING↗
DOE OSTI2024

Towards optimal sensor placement for inverse problems in spaces of measures

The objective of this work is to quantify the reconstruction error in sparse inverse problems with measures and stochastic noise, motivated by optimal sensor placement. To be useful in this context, the error quantities must be explicit in the sensor configuration and robust with respect to the source, yet relatively easy to compute in practice, compared to a direct evaluation of the error by a large number of samples. In particular, we consider the identification of a measure consisting of an unknown linear combination of point sources from a finite number of measurements contaminated by Gaussian noise. The statistical framework for recovery relies on two main ingredients: first, a convex but non-smooth variational Tikhonov point estimator over the space of Radon measures and, second, a suitable mean-squared error based on its Hellinger–Kantorovich distance to the ground truth. To quantify the error, we employ a non-degenerate source condition as well as careful linearization arguments to derive a computable upper bound. This leads to asymptotically sharp error estimates in expectation that are explicit in the sensor configuration. Thus they can be used to estimate the expected reconstruction error for a given sensor configuration and guide the placement of sensors in sparse inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Dust survival rates in clumps passing through the Cas A reverse shock – I. Results for a range of clump densities

ABSTRACT The reverse shock in the ejecta of core-collapse supernovae is potentially able to destroy newly formed dust material. In order to determine dust survival rates, we have performed a set of hydrodynamic simulations using the grid-based code astrobear in order to model a shock wave interacting with clumpy supernova ejecta. Dust motions and destruction rates were computed using our newly developed external, post-processing code paperboats, which includes gas drag, grain charging, sputtering, and grain–grain collisions. We have determined dust destruction rates for the oxygen-rich supernova remnant Cassiopeia A as a function of initial grain sizes and clump gas density. We found that up to $30\,\mathrm{{{\ \rm per\ cent}}}$ of the carbon dust mass is able to survive the passage of the reverse shock if the initial grain size distribution is narrow with radii around ∼10–50 nm for high gas densities, or with radii around $\sim 0.5\!-\!1.5\,\mathrm{\mu m}$ for low and medium gas densities. Silicate grains with initial radii around 10–30 nm show survival rates of up to $40\,\mathrm{{{\ \rm per\ cent}}}$ for medium- and high-density contrasts, while silicate material with micron-sized distributions is mostly destroyed. For both materials, the surviving dust mass is rearranged into a new size distribution that can be approximated by two components: a power-law distribution of small grains and a lognormal distribution of grains having the same size range as the initial distribution. Our results show that grain–grain collisions and sputtering are synergistic and that grain–grain collisions can play a crucial role in determining the surviving dust budget in supernova remnants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Development of a Converter Based Microgrid Test Platform

To overcome the limitations of digital simulation (numerical oscillation, limited computing capability of processors, etc.), a converter-based hardware test-bed was developed at CURENT for real-time power grid emulation. However, distribution systems especially microgrids cannot be emulated and tested on the existing hardware test-bed. This paper develops a microgrid test-bed based on the existing hardware test-bed to enable controller testing for microgrids with dynamic boundary. The design and realization of the microgrid hardware test-bed are introduced. The experimental results of the microgrid controller tests are also provided.

Li, Dingrui↗
DOE OSTI2025

Describing Function Analysis of Transformer Magnetizing Inductance for Direct Power Control of Back-to-Back Modular Multilevel Converters with Advanced Grid Support

This paper provides a detailed investigation into the application of describing function-based analysis for assessing transformer magnetizing inductance and its impact on system performance. The focus is on a back-to-back modular multilevel converter architecture, designed to interconnect systems operating at different frequencies. The study explores the implementation of a Direct Power Control strategy, examining its effects on transformer magnetizing inductance saturation and offering effective mitigation techniques. Furthermore, the integration of advanced grid support functionalities is highlighted, demonstrating how these enhancements bolster the converter's ability to improve grid stability and power quality, positioning it as a robust solution for modern power systems. The proposed approach is validated through extensive computer simulations based MAT LAB/Simulink domain, supported by significant case study results, confirming its practical effectiveness.

back-to-back modular multilevel converters (B2B- M↗
DOE OSTI2023

A Model Calibration Method for Grid-Forming Inverters Using Iterative Bayesian Optimization

As inverter-based resources (IBRs) are rapidly deployed, especially at the distribution and microgrid levels, the need to include their accurate representations in power systems models increases. With well-calibrated models, IBR-interactions can be examined, preventing any stability and operational issues, especially in islanded systems. This paper proposes a method to calibrate generic inverter models using Bayesian optimization, but with a parameter-grouping approach to improve speed and accuracy. This approach is illustrated for a grid-forming inverter using synthetic data and field measurements from a simple IBRbased microgrid. Tests show that the calibration algorithm has modest computation cost and is robust to noisy measurements..

Biswas, Shuchismita↗
DOE OSTI2018

Detailed Chemical Kinetics Based Simulation of Detonation-Containing Flows

Reliable and robust simulations of detonations in inhomogeneous and turbulent environments are of direct importance in the design of rotating detonation engines (RDEs). In particular, computational models will be especially useful in designing and optimizing discrete injectors that introduce fuel and air separately into the detonation chamber, but ensure appropriate level of mixing to sustain detonations but minimize backflow of detonation products and pressure waves into the feed plenums. Since the structure of detonations itself is non-ideal, models have to include a detailed description of this reacting flow in order to be predictive in nature. Here, a highly-scalable open source based solver has been developed for complex detonating flows such that a) the detonation processes are described using detailed chemical kinetics, b) the method is computationally efficient through the introduction of adaptive mesh refinement, and c) the solver can handle complex geometries of relevance to RDE design. Grid convergence of key metrics for detonations is evaluated using canonical flows. Further, the importance of the use of detailed chemical kinetics is illustrated by extracting the composition structure behind a two-dimensional detonation front. Finally, simulations of a practical RDE configuration are used to demonstrate the applicability of this solver to analyzing geometries. The simulation captures the general trends of the experiment well. It is found that the detonation occurs under partially-premixed conditions. Propagation of pressure waves to the injection system is observed which could influence flow behavior in the oxidizer plenum.

03 NATURAL GAS↗
DOE OSTI2022

Grid-tied Multilevel Inverter with Phase-locked Loop Algorithm

A multilevel inverter is an electronic device capable of changing direct current energy to alternant current energy with a voltage and frequency established by the user. They are ideal for connecting renewable energy sources to the AC grid, energy plants, and smart grids. The voltage must be balanced and synchronized with the electrical network for adequate performance. This paper shows the voltage synchronization between an inverter output voltage and the AC grid using a phase-locked loop based on an adaptive observer. The proposed algorithm can perform under grid uncertainties such as noise and generates a reference signal for the modulation used in the inverter. The algorithm is robust and computationally efficient and can be implemented through basic elements such as operational amplifiers, resistors, and capacitors, reducing its difficulty in executing it in a system.

42 ENGINEERING↗
DOE OSTI2014

Cloud Based Applications and Platforms (Presentation)

Presentation to the Cloud Computing East 2014 Conference, where we are highlighting our cloud computing strategy, describing the platforms on the cloud (including Smartgrid.gov), and defining our process for implementing cloud based applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2025

Thermal and electric multidomain dynamic model for integration of power grid distribution with behind-the-meter devices

As renewable energy sources like solar and wind power become more integrated into the grid, coordinated control of behind-the-meter devices is crucial for enhancing grid flexibility and reliability and for meeting cost targets, with standardized models being developed to support this transition. The increasing flexibility and uncertainty of integrated renewable energy grids, along with interactions between various subsystems, make traditional steady-state modeling insufficient to capture transient and dynamic behaviors. Current models (e.g., composite load and battery equivalent models) focus on thermodynamic or electrical characteristics but overlook critical electromechanical interactions. This limits the ability to share performance information for grid services and hampers fast dynamic simulations. In addition, motor stalling is usually triggered by a fault event and attributed to the characteristics of the mechanical torque of the motor, resulting in absorption of a large amount of reactive power during the stalling period. Further, this significant withdrawal of reactive power will deteriorate the dynamic voltage stability of power grids and cause delayed voltage recovery. Therefore, an in-depth modeling of the thermodynamics or mechanical torque is essential to study the impacts of the realistic torque characteristics of those behind-the-meter devices on power system voltage stability. This study developed a dynamic multidomain model for building HVAC systems, such as air-source heat pumps, to simulate their thermal and electrical responses to grid transients. The model can accurately predict power metrics with a mean absolute percentage error of 10%, by validating against with power system computer-aided design performance data. Case studies demonstrate the model capability of capturing the transient response to sudden voltage changes, rapid load fluctuations, and system shutdowns respectively. During a sudden voltage drop (30% for 0.1s), a fully loaded heat pump’s motor speed dropped, continued declining, and shut down after 3.6s, with severe power oscillations and a torque spike. A partially loaded unit experienced temporary oscillations but stabilized. Under higher building loads, compressor speed increased from 64% to 100%, with power and torque rising before stabilizing. In safety-triggered shutdowns, power decreased after minor fluctuations, and torque briefly spiked before dropping to zero.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

ConKer: An algorithm for evaluating correlations of arbitrary order

Context. High order correlations in the cosmic matter density have become increasingly valuable in cosmological analyses. However, computing these correlation functions is computationally expensive. Aims. We aim to circumvent these challenges by developing a new algorithm called ConKer for estimating correlation functions. Methods. This algorithm performs convolutions of matter distributions with spherical kernels using FFT. Since matter distributions and kernels are defined on a grid, it results in some loss of accuracy in the distance and angle definitions. We study the algorithm setting at which these limitations become critical and suggest ways to minimize them. Results. ConKer is applied to the CMASS sample of the SDSS DR12 galaxy survey and corresponding mock catalogs, and is used to compute the correlation functions up to correlation order n = 5. We compare the n = 2 and n = 3 cases to traditional algorithms to verify the accuracy of the new algorithm. We perform a timing study of the algorithm and find that three of the four distinct processes within the algorithm are nearly independent of the catalog size N , while one subdominant component scales as O ( N ). The dominant portion of the calculation has complexity of O ( N c 4/3 log N c ), where N c is the of cells in a three-dimensional grid corresponding to the matter density. Conclusions. We find ConKer to be a fast and accurate method of probing high order correlations in the cosmic matter density, then discuss its application to upcoming surveys of large-scale structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

How will United States commercial building energy use be impacted by IPCC climate scenarios?

Climate change and anthropogenically-forced shift of weather in the future will impact energy use and resilience of both the built environment and the electric grid. The aim of this analysis is to understand how future climate scenarios will impact electricity and natural gas use of commercial buildings in the United States. Here, this study analyzes this impact for 2030, 2045, and 2100 using Representative Concentration Pathways (RCP) scenarios defined in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5. The large, gridded simulation of meteorological variables for RCPs 2.6, 4.5, 6.0, and 8.5 are selected and downscaled to make available hourly Future Meteorological Year (FMY) weather files for use and improvement in subsequent studies. High performance computing resources use these FMYs to simulate commercial prototype buildings in every American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) climate zone of the United States (US), and results are scaled to nation-wide energy use using conditioned floor area multipliers. The analysis is conducted without speculating the physical and performance traits of future buildings or the grid characteristics. This analysis quantifies the impact of climate change on source electrical and natural gas usage for commercial buildings in the United States over the next 80 years. If US commercial floorspace remained constant, total energy use by 2100 is predicted between an 1.75% decrease under the greatest emission scenario (8.5) and a 1.76% increase under the lowest emission scenario (2.6). When adjusted for anticipated urban growth by 2100, the predicted range is 65% increase (8.5) and 71% increase (2.6). Under a global temperature rise climate scenario, the warmest US climate zones will see a large increases in electricity use derived from space cooling while the coldest US climate zones will see significant decreases in natural gas use caused by the decrease in heating necessary. While climate change may ultimately require adaptations of the built environment to withstand its effects and because the United States is a country that requires more heating than cooling, from a building energy perspective, climate change (average temperature rise) is a net energy saver for the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Enhancing ACPF Analysis: Integrating Newton-Raphson Method with Gradient Descent and Computational Graphs

This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy sources in power systems introduces variability and unpredictability, and this method addresses these challenges. It leverages the robustness of NR for accurate approximations and the flexibility of GD for handling variable conditions, all without requiring Jacobian matrix inversion. Furthermore, computational graphs provide a structured and visual framework that simplifies and systematizes the application of these methods. The goal of this fusion is to overcome the limitations of traditional ACPF methods and improve the resilience, adaptability, and efficiency of modern power grid analyses. We validate the effectiveness of our advanced algorithm through comprehensive testing on established IEEE benchmark systems. Furthermore, our findings demonstrate that our approach not only speeds up the convergence process but also ensures consistent performance across diverse system states, representing a significant advancement in power flow computation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

An enhanced AUSM + -up scheme for high-speed compressible two-phase flows on hybrid grids

Here in this work, an enhanced AUSM + -up scheme is presented for high-speed compressible two-phase flows using a six-equation two-fluid single-pressure model. Based on the observation that the AUSM + -up flux function does not take into account relative velocity between the two phases and thus is not stable and robust for computation of two-phase flows involving interaction of strong shock waves and material interfaces, the enhancement is in the form of a volume fraction coupling term and a modification of the velocity diffusion term, both proportional to the relative velocity between the two phases. These modifications in the flux function obviate the need to employ the exact Riemann solver, leading to a significantly less expensive yet robust flux scheme. Furthermore, the Tangent of Hyperbola for INterface Capturing (THINC) scheme is used in order to provide a sharp resolution for material interfaces. A number of benchmark test cases are presented to assess the performance and robustness of the enhanced AUSM + -up scheme for compressible two-phase flows on hybrid unstructured grids. Lastly, the numerical experiments demonstrate that the enhanced AUSM + -up scheme along with THINC scheme can efficiently compute high-speed two-fluid flows such as shock–bubble interactions, while accurately capturing material interfaces.

42 ENGINEERING↗
DOE OSTI2017

Treatment of the polar coordinate singularity in axisymmetric wave propagation using high-order summation-by-parts operators on a staggered grid

In this paper, we develop a high-order finite difference scheme for axisymmetric wave propagation in a cylindrical conduit filled with a viscous fluid. The scheme is provably stable, and overcomes the difficulty of the polar coordinate singularity in the radial component of the diffusion operator. The finite difference approximation satisfies the principle of summation-by-parts (SBP), which is used to establish stability using the energy method. To treat the coordinate singularity without losing the SBP property of the scheme, a staggered grid is introduced and quadrature rules with weights set to zero at the endpoints are considered. Finally, the accuracy of the scheme is studied both for a model problem with periodic boundary conditions at the ends of the conduit and its practical utility is demonstrated by modeling acoustic-gravity waves in a magmatic conduit.

42 ENGINEERING↗
DOE OSTI2024

Interdisciplinary Approaches to Cybervulnerability Impact Assessment for Energy Critical Infrastructure

As energy infrastructure becomes more interconnected, understanding cybersecurity risks to production systems requires integrating operational and computer security knowledge. We interviewed 18 experts working in the field of energy critical infrastructure to compare what information they find necessary to assess the impact of computer vulnerabilities on energy operational technology. These experts came from two groups: 1) computer security experts and 2) energy sector operations experts. We find that both groups responded similarly for general categories of information and displayed knowledge about both domains, perhaps due to their interdisciplinary work at the same organization. Yet, their discussion of each group’s domain-specific training, motivations, and limitations, as well as their suggestions for collaboration across domains, highlighted how these two groups can work together to help each other secure the energy grid. Our findings inform the development of interdisciplinary security approaches in critical-infrastructure contexts.

97 MATHEMATICS AND COMPUTING↗