SEARCH · Engineering Papers
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.
TcN is Tetraauricupride structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Tc3+ is bonded in a body-centered cubic geometry to eight equivalent N3- atoms. All Tc–N bond lengths are 2.32 Å. N3- is bonded in a body-centered cubic geometry to eight equivalent Tc3+ atoms.
TcN is Zincblende, Sphalerite structured and crystallizes in the cubic F-43m space group. The structure is three-dimensional. Tc3+ is bonded to four equivalent N3- atoms to form corner-sharing TcN4 tetrahedra. All Tc–N bond lengths are 1.99 Å. N3- is bonded to four equivalent Tc3+ atoms to form corner-sharing NTc4 tetrahedra.
TcN is Halite, Rock Salt structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. Tc3+ is bonded to six equivalent N3- atoms to form a mixture of edge and corner-sharing TcN6 octahedra. The corner-sharing octahedral tilt angles are 0°. All Tc–N bond lengths are 2.16 Å. N3- is bonded to six equivalent Tc3+ atoms to form a mixture of edge and corner-sharing NTc6 octahedra. The corner-sharing octahedral tilt angles are 0°.
TcN crystallizes in the tetragonal P4_2/mmc space group. The structure is three-dimensional. Tc3+ is bonded in a square co-planar geometry to four equivalent N3- atoms. All Tc–N bond lengths are 2.05 Å. N3- is bonded in a 4-coordinate geometry to four equivalent Tc3+ atoms.
TcN is Tungsten Carbide-like structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Tc3+ is bonded to six equivalent N3- atoms to form a mixture of corner, edge, and face-sharing TcN6 octahedra. The corner-sharing octahedral tilt angles are 44°. All Tc–N bond lengths are 2.17 Å. N3- is bonded to six equivalent Tc3+ atoms to form a mixture of distorted corner and edge-sharing NTc6 pentagonal pyramids.
This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intra- hour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. Here, the framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% - 30% compared with state-of-the-art methods.
Grubbs 3rd-generation (G3) pre-catalyst-initiated ring-opening metathesis polymerization (ROMP) remains an indispensable tool in the polymer chemist's toolbox. Tricyclononenes (TCN) and tricyclononadienes (TCND) represent under-explored classes of monomers for ROMP that have the potential to both advance fundamental knowledge (e.g., structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (e.g., post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their exo-norbornene counterparts, were synthesized to compare their behaviors in G3-initiated ROMP. Real-time 1 H NMR was used to study their polymerization kinetics; propagation rates ( k p ) were extracted for each monomer. To understand the relationships between monomer structure and ROMP propagation rates, density functional theory methods were used to calculate a variety of electronic and steric parameters for each monomer. While electronic parameters (e.g., HOMO energy levels) correlated positively with the measured k p values, steric parameters generally gave improved correlations, which indicates that monomer size and shape are better predictors for k p than electronic parameters for this data set. Furthermore, the TCND diester—which contains an electron-deficient cyclobutene that is resistant to ROMP—and its polymer p(TCND) are shown to be highly reactive toward DBU-catalyzed conjugate addition reactions with thiols, providing a protecting- and activating-group free strategy for post-polymerization modification.
We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.
The Corps Network (TCN) is a national association that represents more than 150 local organizations around the country that work to provide job training and employment to individuals working on projects that provide community benefits. Through its partnership in the Better Buildings Workforce Accelerator, TCN requested technical assistance from the National Renewable Energy Laboratory (NREL) to support Corps organizations involved in – or interested in developing – programs related to energy efficiency in buildings. The goal of this project is to provide industry-recognized energy efficiency education/training models and resources that can be tailored and replicated by Corps across the country, and which can best prepare Corpsmembers to enter the energy efficiency workforce when they complete their terms of service.
Modeling the full-range deformation behaviors of materials under complex loading and materials conditions is a significant challenge for constitutive relations (CRs) modeling. Here, we propose a general encoder-decoder deep learning framework that can model high-dimensional stress-strain data and complex loading histories with robustness and universal capability. The framework employs an encoder to project high-dimensional input information (e.g., loading history, loading conditions, and materials information) to a lower-dimensional hidden space and a decoder to map the hidden representation to the stress of interest. We evaluated various encoder architectures, including gated recurrent unit (GRU), GRU with attention, temporal convolutional network (TCN), and the Transformer encoder, on two complex stress-strain datasets that were designed to include a wide range of complex loading histories and loading conditions. All architectures achieved excellent test results with an root-mean-square error (RMSE) below 1 MPa. Additionally, we analyzed the capability of the different architectures to make predictions on out-of-domain applications, with an uncertainty estimation based on deep ensembles. The proposed approach provides a robust alternative to empirical/semi-empirical models for CRs modeling, offering the potential for more accurate and efficient materials design and optimization.
Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.
The structural depression that occupies the Okavango Basin in southern Africa comprises a depo-centre within the intracratonic Kalahari Basin where sediments of the Cenozoic Kalahari Group have accumulated. The Okavango Basin has been formed due to stretching and subsidence at an area of diffused deformation, southwestwards to the main East African Rift System (EARS). Sediments from two full Kalahari Group sequences, located on opposite sides of the Gumare Fault that forms a major fault within the Okavango Basin, were studied to determine their provenance and chronology. Terrestrial Cosmogenic Nuclide (TCN) 26 Al/ 10 Be burial dating was used to constrain a chronostratigraphical framework, and Pb, Sr, and Nd isotopic ratios combined with geochemical and sedimentological analyses were applied to track the source areas of the sediments.Results indicate the following sequence of basin filling: (a) Accumulation between ca. 4–3 Ma during which the currently downthrown (southern) block received a mixture of sediments mostly from the Choma-Kalomo, Ghanzi-Chobe, and Damara terranes, and possibly from the Lufilian Belt and/or Karoo basalts during earlier stages of deposition. Simultaneously, the upthrown (northern) block received sediments from more distant Archean sources in the Zimbabwe and/or Kasai cratons, (b) Hiatus in sedimentation occurred at both sites between ca. 3–2 Ma, (c) Sediments on both sides of the Gumare Fault share a similar source (Angolan Shield) with minor distinct contributions to the downthrown block from the Kasai Craton and local sources input to the upthrown block, and (d) Regional distribution of aeolian sand since at least 1 Ma. The change in source areas is attributed to rearrangements of the drainage systems that were probably linked to vertical crustal movements on the margins of the Okavango Basin. The tectonically induced morphodynamics controlled the landscape evolution of the endorheic basin where vast lakes, wetlands and salt pans have developed through time.
This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.
Not Available