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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

EMS and DMS Integration of the Coordinative Real-time Sub-Transmission Volt-Var Control Tool under High DER Penetration

This paper proposes an applicable approach to deploy the Coordinative Real-time Sub-Transmission Volt-Var Control Tool (CReST-VCT), and a holistic system integration framework considering both the energy management system (EMS) and distribution system management system (DMS). This provides an architectural basis and can serve as the implementation guideline of CReST-VCT and other advanced grid support tools, to co-optimize the operation benefits of DERs and assets in both transmission and distribution networks. Potential communication protocols for different physical domains of a real application is included. Performance and security issues are also discussed, along with specific considerations for field deployment. Finally, the paper presents a viable pathway for CReST-VCT and other advanced grid support tools, which could be integrated in an open-source standardized-based platform that supports distribution utilities.

Nguyen, Quan H.↗
NASA NTRSDate not supplied

Analysis and Optimization of Baseline Single Aisle Aircraft for Future Electrified Powertrain Flight Demonstrator Comparisons

The purpose of this study is to provide baseline single-aisle vehicles for future comparisons with NASA’s Electrified Powertrain Flight Demonstration (EPFD) turbofan-powered Vision Systems. State-of-the-art single-aisle transports with varying design capacities of 100 to 150 passengers are modeled using NASA Ames Research Center’s General Aviation Synthesis Program (GASP) as well as GASPy. GASPy is a modernized Python-based version of GASP built on the OpenMDAO framework to allow for future, efficient gradient-based optimization and coupled airframe-propulsion design. In order to meet projected NASA Aeronautics goals for 2035, advanced aircraft technologies must be incorporated into these vehicle systems. Methodology to parametrically infuse baseline aircraft models with advanced technologies simulating improvements in aerodynamics, structures, and propulsion systems is detailed, along with the results of technology sensitivity studies. Comparison of the baseline and advanced configurations will allow for future analysis of the benefits of future hybrid and fully electric aircraft concepts in the EPFD project, where fuel consumption and emissions will be modeled and assessed. This study has been conducted under the EPFD project to establish benchmark turbofan models and demonstrate System Analysis capabilities in multi-disciplinary aircraft design, analysis, and optimization for advanced turbofan concepts.

Carl J. Recine↗
DOE OSTI2021

Chemical Heterointerface Engineering on Hybrid Electrode Materials for Electrochemical Energy Storage

Abstract The chemical heterointerfaces in hybrid electrode materials play an important role in overcoming the intrinsic drawbacks of individual materials and thus expedite the in‐depth development of electrochemical energy storage. Benefiting from the three enhancement effects of accelerating charge transport, increasing the number of storage sites, and reinforcing structural stability, the chemical heterointerfaces have attracted extensive interest and the electrochemical performances of hybrid electrode materials have been significantly optimized. In this review, recent advances regarding chemical heterointerface engineering in hybrid electrode materials are systematically summarized. Especially, the intrinsic behaviors of chemical heterointerfaces on hybrid electrode materials are refined based on built‐in electric field, van der Waals interaction, lattice mismatch and connection, electron cloud bias and chemical bond, and their combination. The strategies for introducing chemical heterointerfaces are classified into in situ local transformation, in situ growth, cosynthesis, and other strategy. The recent progress about the chemical heterointerfaces engineering specially focusing on metal‐ion batteries, supercapacitors, and Li–S batteries are introduced in detail. Furthermore, the classification and characterization of chemical heterointerfaces are briefly described. Finally, the emerging challenges and perspectives about future directions of chemical heterointerface engineering are proposed.

Li, Wenbin↗
DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

New Technologies for Power System Operation and Analysis, 1st Edition

New Technologies for Power System Operation and Analysis considers the very latest developments in renewable energy integration and system operation, including electricity markets and wide-area monitoring systems and forecasting. Helping readers quickly grasp the essential information needed to address renewable energy integration challenges, this new book looks at basic power system mathematical models, advanced renewable integration and system optimizations from transmission and distribution system sides. Sections cover wind, solar, gas and petroleum, making this a useful reference for all engineers interested in power system operation.

electricity markets↗
DOE OSTI2025

Effect of grafting density on the two-dimensional assembly of nanoparticles

Employing grazing-incidence small-angle X-ray scattering (GISAXS) and X-ray reflectivity (XRR), we demonstrate that films composed of polyethylene glycol (PEG)-grafted silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs), as well as their binary mixtures, form highly stable hexagonal structures at the vapor–liquid interface. These nanoparticles exhibit remarkable stability under varying environmental conditions, including changes in pH, mixing concentration, and PEG chain length. Short-chain PEG grafting produces dense, well-ordered films, while longer chains produce more complex, less dense quasi-bilayer structures. AuNPs exhibit higher grafting densities than AgNPs, leading to more ordered in-plane arrangements. In binary mixtures, AuNPs dominate the population at the surface, while AgNPs integrate into the system, expanding the lattice without forming a distinct binary superstructure. In conclusion, these results offer valuable insights into the structural behavior of PEG-grafted nanoparticles and provide a foundation for optimizing binary nanoparticle assemblies for advanced nanotechnology applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Decomposition-Based Learn-To-Optimize Approach with Feasibility Layer Assistance for Sub-Hourly Unit Commitment

Sub-hourly unit commitment (UC) with 15-min intervals is gaining significant attention as a way to respond rapidly to the fluctuations in electricity supply and demand introduced by renewable resources. However, the increased temporal resolution and complex inter-temporal dependencies pose substantial computational challenges for traditional optimization methods. To this end, this paper explores a decomposition-based learn-to-optimize approach. Building on recent advances in machine learning, our method revisits the long- overlooked Lagrangian relaxation framework, which is a classical decomposition technique that enables tractable subproblem solving. These smaller subproblems are inherently well-suited for machine learning, as their reduced dimensionality and structural regularity allow predictive models to efficiently learn and generalize solution patterns. We thus propose a generic predictive model, which embeds Gated Recurrent Units (GRUs) and Attention in the encoder-decoder structure, and integrate a rule-based feasibility layer to capture temporal dependencies, reduce training effort, and improve feasibility w.r.t. unit-level constraints. Our method has been validated on the IEEE 118-bus system, demonstrating promising performance in solving sub-hourly UC problems efficiently and feasibly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Neoadjuvant Sandwich Treatment With Oxaliplatin and Capecitabine Administered Prior to, Concurrently With, and Following Radiation Therapy in Locally Advanced Rectal Cancer: A Prospective Phase 2 Trial

Purpose: Systemic failure remains the major challenge in management of locally advanced rectal cancer (LARC). To optimize the timing of neoadjuvant treatment and enhance systemic control, we initiated a phase 2 trial to evaluate a new strategy of neoadjuvant sandwich treatment, integrating induction chemotherapy, concurrent chemoradiation therapy, and consolidation chemotherapy. Here, we present preliminary results of this trial, reporting the tumor response, toxicities, and surgical complications. Methods and Materials: Fifty-one patients with LARC were enrolled, among which were two patients who were ineligible because of distant metastases before treatment. Patients were treated first with one cycle of induction chemotherapy consisting of oxaliplatin, 130 mg/m² on day 1, with capecitabine, 1000 mg/m² twice daily for 14 days every 3 weeks (the XELOX regimen), followed by chemoradiation therapy, 50 Gy over 5 weeks, with the modified XELOX regimen (oxaliplatin 100 mg/m²), and then with another cycle of consolidation chemotherapy with the XELOX regimen. Surgery was performed 6 to 8 weeks after completion of radiation therapy. Tumor responses, toxicities, and surgical complications were recorded. Results: All but one patent completed the planned schedule of neoadjuvant sandwich treatment. Neither life-threatening blood count decrease nor febrile neutropenia were observed. Forty-five patents underwent optimal surgery with total mesorectal excision (TME). Four patients refused surgery because of clinically complete response. There was no perioperative mortality in this cohort. Five patients (11.1%) developed postoperative complications. Among the 45 patients who underwent TME, pathologic complete response (pCR), pCR or major regression, and at least moderate regression were achieved in 19 (42.2%), 37 (82.2%), and 44 patients (97.8%), respectively. Conclusions: Preliminary results suggest that the strategy of neoadjuvant sandwich treatment using XELOX regimen as induction, concomitant, and consolidation chemotherapy to the conventional radiation is well tolerated. The strategy is highly effective in terms of pCR and major regression, which warrants further investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Uranium nitride tristructural-isotropic fuel particle

A summary of activities around concept development, design, manufacturing, and early irradiation testing of a novel coated fuel particle, uranium nitride tristructural-isotropic fuel is provided. This fuel particle offers significantly higher uranium density over historic manifestations of coated fuel particles and may be more optimal for a range of advanced reactor applications. After reviewing the design process, steps involving production of this fuel form are discussed. Low burnup irradiation data on this fuel particle are now available, indicating that performance metrics have been met to date based on the original design expectations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.

25 ENERGY STORAGE↗
DOE OSTI2025

Temperature-dependent mechanism on hardening mitigation in thermomechanically processed 800H alloys under neutron irradiation

Irradiation induced hardening is commonly observed in structural materials, and is often associated with the degraded ductility. This study explores the role of thermomechanical processing (TMP) in mitigating irradiation-induced hardening in Incoloy 800H. The radiation response of solution-annealed and TMP-treated samples was systematically investigated to elucidate the underlying mechanisms of hardening reduction. Neutron irradiation was performed on solution-annealed and TMP-treated samples at 359, 431, and 580 °C, revealing consistently lower hardening in TMP samples. Microstructural characterization compared dislocation loops, cavities, clusters, and precipitates to assess their contributions to hardening. The strengthening contributions from individual microstructural features were estimated, and the microstructure-derived yield strength is consistent with the hardness-converted yield strength. The microstructure–properties correlation revealed the temperature-dependent primary hardening mechanism: dislocation loops dominated at lower temperature, whereas Ni3(Al,Ti) γ′ precipitates were predominant at higher temperature. TMP reduced radiation-induced hardening through two mechanisms: (1) increased sink strength from dislocations and Ti(C,N) precipitates limited dislocation loop formation at lower temperatures, and (2) reduced Ti solute within the matrix in TMP samples suppressed γ′ precipitate formation at higher temperatures. These findings identify the mechanisms governing radiation-induced hardening across different temperatures, which lays the foundation for TMP optimization for the development of advanced radiation-tolerant materials.

Zhong, Weicheng [ORNL] (ORCID:0000000231589271)↗
DOE OSTI2025

Evolution of Mass Spectrometers for High m / z Biological Ion Formation, Transmission, Analysis and Detection: A Personal Perspective

Mass spectrometry (MS) has become an essential tool in virtually all academic, pharmaceutical, and biopharmaceutical analytical laboratories. The specialized and bespoke area of MS research and application of high m / z ion (> m / z 6000 and high mass, >150 kDa) formation, transmission, analysis, and detection is a relatively new area of focus for MS that has seen dramatic acceleration in interest over the last two decades. Herein we delve into this exciting aspect of MS, discussing how MS instrumentation has been refined and evolved for native-MS analysis. We cover the early groundbreaking experiments showing high m / z ion formation, transmission, and preservation of protein structure in the gas phase. Additionally, we discuss specific instrument optimizations and modifications that have advanced high m / z ion generation, transmission, analysis, and detection, contributing to the research area known as gas-phase structural biology. Native-MS sample introduction methods, emerging technologies, and future perspectives are also examined. Finally, we share personal opinions, observations, and experiences that are new to the community or previously unpublished.

collisions↗
DOE OSTI2022

Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation

The synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary interfacing technology for many renewable sources of energy, have supplanted synchronous generators in ever growing numbers, the center of attention on associated integration challenges have resided primarily on the role of declining system inertia. Our results instead highlight the critical role of generator damping in achieving a stable state of synchronization. Additionally, we report the feasibility of operating interconnected electric grids with up to 100% power contribution from inverter-based renewable generation technologies. Our study has important implications as it sets the basis for the development of advanced control architectures and grid optimization methods that ensure synchronization and further pave the path towards the decarbonization of the electric power sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗
DOE OSTI2022

Surface reaction for efficient and stable inverted perovskite solar cells

We report perovskite solar cells (PSCs) with an inverted structure (often referred to as the p-i-n architecture) are attractive for future commercialization due to their easily scalable fabrication, reliable operation, and compatibility with a wide range of perovskite-based tandem device architectures. However, the power conversion efficiency (PCE) of p-i-n PSCs falls behind n-i-p (or normal) structure counterparts. This large performance gap could undermine efforts to adopt p-i-n architectures, despite their other advantages. Given the remarkable advances in perovskite bulk materials optimization over the past decade, interface engineering has become the most important strategy to push PSC performance to its limit. Here, we report a reactive surface engineering approach based on a simple post-growth treatment of 3-(Aminomethyl)pyridine (3-APy) on top of a perovskite thin film. First, the 3-APy molecule selectively reacts with surface FA+, reducing perovskite surface roughness and surface potential fluctuations associated with surface steps/terraces. Second, the reaction product on the perovskite surface decreases the formation energy of charged iodine-vacancies, leading to effective n-type doping with a reduced work function in the surface region. With this reactive surface engineering, the resulting p-i-n PSCs obtained a PCE over 25%, along with retaining 87% of the initial PCE after over 2400 h of one-sun operation at about 55 degrees C in air.

14 SOLAR ENERGY↗
DOE OSTI2025

Theoretical foundations of waste factor and waste figure with applications to fixed wireless access and relay systems

The growing energy demands of next-generation wireless systems call for unified, system-level metrics to evaluate and optimize energy efficiency. This paper advances the concept of the Waste Factor (W), or Waste Figure (WF) in decibel scale, as a general framework for modeling power loss across cascaded communication components. By integrating W into the Consumption Factor (CF)–the ratio of data rate to consumed power–we reveal how component inefficiencies influence the minimum achievable energy per bit. Closed-form expressions are derived for energy-per-bit consumption in both direct and relay-assisted links, along with a decision rule for selecting the more energy-efficient path. While not explicitly modeled, Reflective Intelligent Surfaces (RIS) are shown to fit naturally within this framework. The analysis is further applied to a Fixed Wireless Access (FWA) scenario, where asymmetries in traffic direction and hardware inefficiencies are jointly considered, demonstrating the utility of the Waste Factor in guiding energy-aware system design.

Sevim, Nurullah [Texas A&M University, College Sta↗