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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Seminar 13 - Advanced Methods for Grid Integration of High-Performance Residential Communities

High-performance residential communities such as net-zero energy (NZE) and NZE-ready communities are emerging across the United States as many cities and states have committed to achieve ambitious energy efficiency and renewable energy goals between now and 2050. The achievement of these goals will transform the nation's communities into the holistic paradigms consisting of pervasive solar photovoltaics (PVs) and other renewable distributed energy resources, energy storage and flexible loads. However, integrating residential communities with high PV penetration may bring many challenges to the grid. For example, during many hours of the year, electric power is flowing out of PV-enabled homes and back into the grid. When this happens simultaneously in many nearby homes, distribution feeder voltage can be raised significantly. Intermittent solar power can also cause voltage flickers, leading to potential damages on both distribution feeder and home appliances. Existing solutions, such as PV curtailment, battery storage, and direct load control, are not able to address all the challenges in a cost-effective way. Better understanding is needed on how to deploy rooftop PV, how to predict building loads, and how to coordinate the homes in a community to enhance grid reliability. This seminar consists of four integral presentations that cover different aspects of a solution to these challenges. First, we will present an automated method to identify the best surfaces to deploy PV and size PV according to the PV penetration goals in a community. Second, we will present a streamlined way to create gray-box building models that are suitable for large-scale building-grid co-simulation. Next, we will present a data-driven method to estimate the end-use energy consumption to better coordinate the behind-the-meter resources to reduce utility cost, provide grid services, and improve renewable energy integration. Lastly, we will present a hierarchical control system that integrates the building models and the estimated non-dispatchable loads into the decision-making process to enhance the reliability and resilience of the residential community. Simulation results from an NZE-ready community in Fort Collins, Colorado will be presented in all four presentations and the guidelines on better grid integration of residential communities will be provided.

14 SOLAR ENERGY↗
DOE OSTI2023

DQ Admittance Extraction for Inverter-Based Resources: Preprint

The power grid industry is pushing for electro-magnetic transient (EMT)-based studies for generation interconnection and planning process due to high penetrations of inverter-based resource (IBRs). Vendor-specific and site-specific black-box IBR models are preferred in those simulation studies. For small-signal analysis, measurement-based admittance models are necessary. In this paper, we demonstrate the extraction of frequency-domain dq frame IBR models. These linear models are specific to operating conditions. We demonstrate two extraction methods: frequency scan and a step response-based method. The latter relies on converting time-domain responses to Laplace-domain expressions via eigensystem realization analysis (ERA). Both can lead to dq admittance representation and the latter is time saving.

admittance model↗
DOE OSTI2023

Image Characterization of Reactions Generated by an Aeroengine Micromixing Injector for Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blend

An aeroengine micromixing injector, originally designed for lean direct injection of jet fuel, was adapted to work with hydrogen, natural gas, and any blend in between. The ultimate goal of the design was to achieve low NOx emissions when operated on pure hydrogen; to better characterize its performance, flame imaging diagnostics was implemented. Three types of cameras mounted parallel to the injector base and pointing towards the quartz combustor cylinder with the same angle were operated simultaneously: Nikon D90, Dynacolor FB-N9-U, and Phantom v7.1, to capture the visible spectrum, OH* chemiluminescence, and dynamic behavior of the flames, respectively. The current work also includes the overlapping of visible spectrum and OH* chemiluminescence, as an effort to qualitatively define the heat release over the flame area, i.e., UV over visible domain. Additionally, a z-type schlieren configuration was employed to reinforce the presence of some small-scale details occurring near the injector outlet ports. Using the airbox pressure drop, preheat temperature, fuel composition and flame temperature as the factors of study, a Box Behnken model was designed yielding into a 16-points matrix for the operability region, at atmospheric pressure. The main responses were extracted from the OH* images, because of its correlation with the flame heat release, these being the flame area and its average brightness, the heat release area and its center of gravity and leading edge. To assess the importance of the main factors and their interactions, an analysis of variance (ANOVA) was performed for each response, considering as significant each contributor with a p-value below 0.05. For these five responses it was found that all four main factors need to be included into the model (most of them because its own significance, others to ensure the model hierarchy), as well as some common interactions as the preheat and flame temperatures or pressure drop and fuel composition, and quadratic terms from fuel composition or flame temperature. Three of the five models presented R2 and coefficient of variance (C.V.) indicators around 0.97 and 5%, respectively; the leading edge ANOVA yielded into R2=0.78 and C.V.=18.88%; and the heat release area showed R2=0.91 and C.V.=24.13%. Lastly, the confinement ratio effect of the combustor over the flame structure was quantified, for three different conditions: the original 80 mm inner diameter and 200 mm length cylinder was compared against a shorter 150 mm tube (same inner diameter) and a narrower 47 mm inner diameter one (same 200 mm length). The length had a smaller effect than the inner diameter, even though both factors were not strong enough to make a significant deviation on most of the flame parameters: the difference was within the repeatability margin of error for the brightness, flame area, and center of gravity and leading edge of the heat release area.

Imaging diagnostics, flame characterization, OH* c↗
NASA NTRS1987

Further generalization of an equivalent plate representation for aircraft structural analysis

Recent developments from a continuing effort to provide an equivalent plate representation for aircraft structural analysis are described. Previous work provided an equivalent plate analysis formulation that is capable of modeling aircraft wing structures with a general planform such as cranked wing boxes. However, the modeling is restricted to representing wing boxes having symmetric cross sections. Further developments, which are described, allow modeling of wing cross sections having asymmetries that can arise from airfoil camber or from thicknesses being different in the upper and lower cover skins. An implementation of thermal loadings, which are described as temperature distributions over the planform of the cover skins, has been included. Spring supports have been added to provide for a more general set of boundary conditions. Numerical results are presented to assess the effect of wing camber on the static and dynamic response of an example wing structure under pressure and thermal loading. These results are compared with results from a finite element analysis program to indicate how well a cambered wing box can be represented with an equivalent plate formulation.

Giles, Gary L.↗
NASA NTRS1987

Further generalization of an equivalent plate representation for aircraft structural analysis

Recent developments from a continuing effort to provide an equivalent plate representation for aircraft structural analysis are described. Previous work provided an equivalent plate analysis formulation that is capable of modeling aircraft wing structures with a general planform such as cranked wing boxes. However, the modeling is restricted to representing wing boxes having symmetric cross sections. Further developments, which are described in this paper, allow modeling of wing cross section having asymmetries that can arise from airfoil camber or from thicknesses being different in the upper and lower cover skins. An implementation of thermal loadings, which are described as temperature distributions over the planform of the cover skins, has been included. Spring supports have been added to provide for a more general set of boundary conditions. Numerical results are presented to assess the effect of wing camber on the static and dynamic response of an example wing structure under pressure and thermal loading. These results are compared with results from a finite element analysis program to indicate how well a cambered wing box can be represented with an equivalent plate formulation.

Giles, Gary L.↗
DOE OSTI2018

Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient supply, temperature, and atmospheric CO 2

Marine phytoplankton stoichiometry links nutrient supply to marine carbon export. Deviations of phytoplankton stoichiometry from Redfield proportions (106C :1P) could therefore have a significant impact on carbon cycling, and understanding which environmental factors drive these deviations may reveal new mechanisms regulating the carbon cycle. To explore the links between environmental conditions, stoichiometry, and carbon cycling, we compared four different models of phytoplankton C :P: a fixed Redfield model, a model with C : P given as a function of surface phosphorus concentration (P), a model with C P given as a function of temperature, and a new multi-environmental model that predicts C : P as a function of light, temperature, and P. These stoichiometric models were embedded into a five-box ocean circulation model, which resolves the three major ocean biomes (high-latitude, subtropical gyres, and tropical upwelling regions). Contrary to the expectation of a monotonic relationship between surface nutrient drawdown and carbon export, we found that lateral nutrient transport from lower C: P tropical waters to high C: P subtropical waters could cause carbon export to decrease with increased tropical nutrient utilization. It has been hypothesized that a positive feedback between temperature and p CO 2 atm will play an important role in anthropogenic climate change, with changes in the biological pump playing at most a secondary role. Here we show that environmentally driven shifts in stoichiometry make the biological pump more influential, and may reverse the expected positive relationship between temperature and p CO 2,atm . In the temperature-only model, changes in tropical temperature have more impact on the Δ p CO 2,atm (~41 ppm) compared to subtropical temperature changes (~4.5 ppm). Our multi-environmental model predicted a decline in p CO 2,atm of ~ 46 ppm when temperature spanned a change of 10 °C. Thus, we find that variation in marine phytoplankton stoichiometry and its environmental controlling factors can lead to nonlinear controls on p CO 2,atm , implying the need for further studies of ocean C : P and the impact on ocean carbon cycling.

58 GEOSCIENCES↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
NASA NTRS1984

Energy dependence of the P(bar)/P ratio in cosmic rays

The antiproton/proton /P(bar)/P/ratio is presented for seven kinetic energy intervals from 4.4 - 13.4 GeV. The P(bar) were observed in 1979 by the New Mexico State University balloon-borne magnet spectrometer. The observations are compared to various models including secondary production in a leaky box. A decline in the P(bar)/P ratio at low energies is expected if the P(bar) are produced as secondaries. The data are consistent with such a decline but the P(bar)/P ratio is much higher than expected. The confidence level is less than 0.0001 for a model based on a leaky box with a mean-matter-traversed lambda of 7 gm/sq cm. The best leaky-box-model fit obtained by varying lambda gives a confidence level of 0.12 for lambda l= 21 + or - 3 gm/sq c. Results are also presented for fits based on the closed-galaxy and other models.

Golden, R. L.↗
DOE OSTI2022

Model-based predictive control of multi-zone commercial building with a lumped building modelling approach

Here this study investigates the applicability of a lumped building modeling approach to model-based predictive control (MPC) to alleviate the complex modeling process of the grey-box multi-zone building model. Based on experimental data, two building models were estimated in this study. The detailed model as a reference case and a lumped model were estimated with decentralized and conventional approaches, respectively. Then, simulations were performed with two boundary conditions, including the comfort bound and electricity cost structure. The performances of the MPC with the detailed and lumped models were analyzed compared to the feedback control. More savings was achieved with a larger comfort bound and more aggressive electricity cost structure. The savings potential of the proposed lumped model approach was not as high as that of the detailed model. However, the proposed method yields good control performance, whose savings was approximately 8.6% over that of feedback control. These results suggest that the proposed method can be used to facilitate MPC implementation in multi-zone building applications.

42 ENGINEERING↗
DOE OSTI2018

The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAIC) Atmosphere Science Plan

Arctic climate change is amplified relative to global change, and is embodied by a dramatic decline in the perennial sea-ice pack. These cryospheric transitions carry significant implications for regional resource development, geopolitics, and global climate patterns. Indeed, the changing arctic cryosphere is considered a grand challenge for global climate research. Arctic change, and its linkages with the global system, must be understood. Additionally, there is a growing stakeholder community that requires improved sea-ice forecasting for many applications. To make progress on these important issues requires developing a detailed, process-level understanding of the coupled climate system that can help to address the numerous deficiencies in numerical model representations of the Arctic. Such an understanding is only possible by making targeted, interdisciplinary measurements within the central arctic sea-ice environment. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative has been developed in response to these great challenges. It comprises three parts: 1) An intensive, icebreaker-based observatory that will freeze in, and drift with, the arctic sea ice for a full annual cycle making interdisciplinary measurements in the atmosphere, sea ice, upper ocean, and biosphere; 2) A distributed network of targeted, autonomous measurements to characterize spatial variability on model grid-box scales; and 3) Coordinated, multiscale analysis and modeling activities. MOSAiC is a major, international effort that will involve participation of many different agencies and entities. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is uniquely positioned to play a critical role in this initiative by providing a comprehensive instrument suite to characterize the atmosphere and its interactions with the sea-ice surface. ARM will deploy its second Mobile Facility (AMF2) and Mobile Aerosol Observing System (MAOS) at the MOSAiC central observatory as it drifts through the central Arctic for a 13-month campaign starting in September 2019. Sea-ice is an integrator of energy fluxes in the coupled arctic climate system; thus, atmosphere-ice-ocean processes impacting the flow of energy through this system in all seasons are the primary target of MOSAiC. The broader, collaborative MOSAiC initiative will allow for many interdisciplinary studies along this theme. ARM’s involvement will target specific areas related to the atmosphere and atmosphere-surface interactions that are critically under-observed in the Arctic, are leading contributors to model uncertainties in the region, and are programmatically important to DOE research and modeling programs. The guiding science themes for this project include: (1) The surface energy budget of sea ice; (2) Clouds and precipitation; (3) Aerosols; and (4) The atmospheric boundary layer. Many fundamental issues concerning these themes are lacking in observational constraints, particularly in the arctic winter and through consecutive seasons. For example, little is known about the energy budget of first-year sea ice, the annual cycle of central arctic aerosol concentrations, the spatial organization of cloud-precipitation systems relative to sea-ice heterogeneity, the winter boundary-layer evolution, or cloud-surface coupling processes over sea ice. The proposed ARM observations will be groundbreaking in many ways, and will ultimately have a dramatic impact on the arctic research community and its ability to represent coupled arctic processes in numerical models.

North Slope of alaska, Arctic sea ice, Barrow, Arc↗
NASA NTRS1995

Operational improvements of long-term predicted ephemerides of the Tracking and Data Relay Satellites (TDRSs)

Tracking and Data Relay Satellite (TDRS) orbit determination and prediction are supported by the Flight Dynamics Facility (FDF) of the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD). TDRS System (TDRSS)-user satellites require predicted TDRS ephemerides that are up to 10 weeks in length. Previously, long-term ephemerides generated by the FDF included predictions from the White Sands Complex (WSC), which plans and executes TDRS maneuvers. TDRSs typically have monthly stationkeeping maneuvers, and predicted postmaneuver state vectors are received from WSC up to a month in advance. This paper presents the results of an analysis performed in the FDF to investigate more accurate and economical long-term ephemerides for the TDRSs. As a result of this analysis, two new methods for generating long-term TDRS ephemeris predictions have been implemented by the FDF. The Center-of-Box (COB) method models a TDRS as fixed at the center of its stationkeeping box. Using this method, long-term ephemeris updates are made semiannually instead of weekly. The impulse method is used to model more maneuvers. The impulse method yields better short-term accuracy than the COB method, especially for larger stationkeeping boxes. The accuracy of the impulse method depends primarily on the accuracy of maneuver date forecasting.

Kostoff, J. L.↗
DOE OSTI2025

Review of Grey Box/Black Box Data Contamination Metrics on Open and Commercial Models

Dataset contamination is a problem where benchmarks and tasks used to evaluate the capabilities of Large Language Models (LLMs) have been incorporated into the training dataset of the models. This gives a false sense of performance that can overestimate how these models will function on truly unseen data. This problem becomes worse with commercial LLMs with larger and non-accessible training data, so techniques have been developed to try to measure the degree to which a model is contaminated with a benchmark’s data. To understand the effectiveness of these techniques, particularly when evaluating contamination on coding tasks, we review trends and categorize techniques by the degree of access to the model that is required. The research literature on this topic has reported mixed effectiveness of these techniques, so we select a set of black box (text access only) and grey box (access to model loss/probabilities required) techniques and apply them to both commercial and non-commercial models. We implement these metrics as part of a framework to test the contamination of Python code in LLMs to see to what extent we can replicate the effectiveness (or ineffectiveness) of these contamination detection techniques. Though we find mixed results in the capabilities of these metrics to identify contamination, we do observe evidence that they can identify contamination (broadly) in fine-tuned models when both a baseline and fine-tuned model is present. Additionally, similarity metrics were able to identify between contaminated and uncontaminated data even in situations where the data is distributionally similar (e.g., drawn from the same set of code projects).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Molecular envelope and atomic model of an anti-terminated glyQS T-box regulator in complex with tRNAGly

A T-box regulator or riboswitch actively monitors the levels of charged/uncharged tRNA and participates in amino acid homeostasis by regulating genes involved in their utilization or biosynthesis. It has an aptamer domain for cognate tRNA recognition and an expression platform to sense the charge state and modulate gene expression. These two conserved domains are connected by a variable linker that harbors additional secondary structural elements, such as Stem III. The structural basis for specific tRNA binding is known, but the structural basis for charge sensing and the role of other elements remains elusive. To gain new structural insights on the T-box mechanism, a molecular envelope was calculated from small angle X-ray scattering data for the Bacillus subtilis glyQS T-box riboswitch in complex with an uncharged tRNAGly. A structural model of an antiterminated glyQS T-box in complex with its cognate tRNAGly was derived based on the molecular envelope. It shows the location and relative orientation of various secondary structural elements. The model was validated by comparing the envelopes of the wild-type complex and two variants. The structural model suggests that in addition to a possible regulatory role, Stem III could aid in preferential stabilization of the T-box anti-terminated state allowing read-through of regulated genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Parameterization of generic positive sequence models to represent behavior of inverter based resources in low short circuit scenarios

The connection of large inverter based resources (IBR) in transmission systems is often located geographically and electrically far away from load centers. This, coupled with the displacement of synchronous machine plants, results in a reduction of the network short circuit strength at the point of connection. Under these conditions, state-of-the-art positive sequence simulation platforms and models can have difficulties maintaining numerical stability and/or providing an accurate representation of IBR plant dynamic behavior. As a result, computationally heavy time domain electromagnetic transient (EMT) simulations may be required to evaluate these systems. In this paper, a recently developed improved generic positive sequence model is parameterized to provide a representation of IBR behavior in low short circuit networks. Further, comparisons against generic and detailed EMT models demonstrate the suitability of the improved positive sequence model to study practical stability issues experienced with presently in-service plants. Such a model can provide some of the accuracy of an EMT representation with a much lower computational burden. The performance of the positive sequence model is validated against the behavior shown by both open white box and closed black box EMT domain models from around the world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1993

Modeling and new equipment definition for the vibration isolation box equipment system

Our MSAD-funded research project is to provide numerical modeling support for the VIBES (Vibration Isolation Box Experiment System) which is an IML2 flight experiment being built by the Japanese research team of Dr. H. Azuma of the Japanese National Aerospace Laboratory. During this reporting period, the following have been accomplished: A semi-consistent mass finite element projection algorithm for 2D and 3D Boussinesq flows has been implemented on Sun, HP And Cray Platforms. The algorithm has better phase speed accuracy than similar finite difference or lumped mass finite element algorithms, an attribute which is essential for addressing realistic g-jitter effects as well as convectively-dominated transient systems. The projection algorithm has been benchmarked against solutions generated via the commercial code FIDAP. The algorithm appears to be accurate as well as computationally efficient. Optimization and potential parallelization studies are underway. Our implementation to date has focused on execution of the basic algorithm with at most a concern for vectorization. The initial time-varying gravity Boussinesq flow simulation is being set up. The mesh is being designed and the input file is being generated. Some preliminary 'small mesh' cases will be attempted on our HP9000/735 while our request to MSAD for supercomputing resources is being addressed. The Japanese research team for VIBES was visited, the current set up and status of the physical experiment was obtained and ongoing E-Mail communication link was established.

Sani, Robert L.↗
DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impacts of stochastic coalescence variability on warm rain initiation using Lagrangian microphysics in box and large-eddy simulations

Various coalescence methods for Lagrangian microphysics schemes are tested in box and large-eddy simulation (LES) models, including the stochastic all-or-nothing super-droplet method (SDM) and a version of SDM (dSDM) that applies a fractional approach similar to the average impact method. In LES, variabilities driven by microphysics and by flow realizations are separated using the “piggybacking” technique. Rain initiation averaged over many realizations of the box model is delayed and rain variability increases as the number of super-drops per collision volume ( N SD ) is decreased using SDM. In contrast, rain initiation time using SDM in LES is insensitive to N SD for 32 ≤ N SD ≤ 512. This is explained through the interaction between LES grid boxes, each acting as a separate collision volume. Variability across the ensemble of LES collision volumes using SDM results in rain quickly initiating in some of the LES grid cells at low N SD and leading to a similar overall timing of rain initiation from the cloud compared to simulations with high N SD . There is a ~20% decrease in the total rain mass and mean rain flux as N SD is increased from 32 to 256, with little additional change as N SD is increased from 256 to 512. The fractional coalescence approach in dSDM leads to reduced microphysical variability and a 15-18 min delay in rain initiation compared to SDM. Finally, an additional LES ensemble with microphysical variability feeding back to the dynamics shows that flow variability dominates the impact of microphysical variability on rain properties. Thus, flow variability must be constrained to isolate impacts of microphysical variability.

54 ENVIRONMENTAL SCIENCES↗