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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 199 records · Page 11

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NASA NTRS1999

Airplane Mesh Development with Grid Density Studies

Automatic Grid Generation Wish List Geometry handling, including CAD clean up and mesh generation, remains a major bottleneck in the application of CFD methods. There is a pressing need for greater automation in several aspects of the geometry preparation in order to reduce set up time and eliminate user intervention as much as possible. Starting from the CAD representation of a configuration, there may be holes or overlapping surfaces which require an intensive effort to establish cleanly abutting surface patches, and collections of many patches may need to be combined for more efficient use of the geometrical representation. Obtaining an accurate and suitable body conforming grid with an adequate distribution of points throughout the flow-field, for the flow conditions of interest, is often the most time consuming task for complex CFD applications. There is a need for a clean unambiguous definition of the CAD geometry. Ideally this would be carried out automatically by smart CAD clean up software. One could also define a standard piece-wise smooth surface representation suitable for use by computational methods and then create software to translate between the various CAD descriptions and the standard representation. Surface meshing remains a time consuming, user intensive procedure. There is a need for automated surface meshing, requiring only minimal user intervention to define the overall density of mesh points. The surface mesher should produce well shaped elements (triangles or quadrilaterals) whose size is determined initially according to the surface curvature with a minimum size for flat pieces, and later refined by the user in other regions if necessary. Present techniques for volume meshing all require some degree of user intervention. There is a need for fully automated and reliable volume mesh generation. In addition, it should be possible to create both surface and volume meshes that meet guaranteed measures of mesh quality (e.g. minimum and maximum angle, stretching ratios, etc.).

Cliff, Susan E.↗

DOE OSTI2025

Smart Meter Data: A Gateway for Reducing Solar Soft Costs with Model-Free Hosting Capacity Maps

Public-facing solar hosting capacity (HC) maps, which show the maximum amount of solar energy that can be installed at a location without adverse effects, have proven to be a key driver of solar soft cost reductions through a variety of pathways (e.g., streamlining interconnection, siting, and customer acquisition processes). However, current methods for generating HC maps require detailed grid models and time-consuming simulations that limit both their accuracy and scalability—today, only a handful out of almost 2,000 utilities provide these maps. This project developed and validated data-driven algorithms for calculating solar HC using data from AMI without the need of detailed grid models or simulations. The algorithms were validated on utility datasets and incorporated as an application into NRECA’s Open Modeling Framework (OMF.coop) for the over 260 coops and vendors throughout the US to use. The OMF is free and open-source for everyone.

14 SOLAR ENERGY↗

DOE OSTI2024

Slide Decks for EV@S Stakeholder Meeting at INL September 2024

These slides were used at the Electric Vehicles at Scale National Lab Consortium, Stakeholder Meeting hosted at INL in September 2024. The slides are now requested to be made available publicly for reference. All slides review projects that are already in the public domain and their information is intended for public release. They are not from sensitive topics or areas with any restrictions. A few projects are overviewed in detail and some slides were just an aid to the breakout sessions that were conducted with stakeholders and lab staff.

25 ENERGY STORAGE↗

NASA NTRS1985

Experiments in structural dynamics and control using a grid

Future spacecraft are being conceived that are highly flexible and of extreme size. The two features of flexibility and size pose new problems in control system design. Since large scale structures are not testable in ground based facilities, the decision on component placement must be made prior to full-scale tests on the spacecraft. Control law research is directed at solving problems of inadequate modelling knowledge prior to operation required to achieve peak performance. Another crucial problem addressed is accommodating failures in systems with smart components that are physically distributed on highly flexible structures. Parameter adaptive control is a method of promise that provides on-orbit tuning of the control system to improve performance by upgrading the mathematical model of the spacecraft during operation. Two specific questions are answered in this work. They are: What limits does on-line parameter identification with realistic sensors and actuators place on the ultimate achievable performance of a system in the highly flexible environment? Also, how well must the mathematical model used in on-board analytic redundancy be known and what are the reasonable expectations for advanced redundancy management schemes in the highly flexible and distributed component environment?

Montgomery, R. C.↗

DOE OSTI2025

Virtual Power Plants and Distributed Energy Resource Management Systems

Virtual Power Plants (VPPs) are aggregations of DERs that can balance electrical loads and provide utility-scale and utility-grade grid services like a traditional power plant. This presentation covers VPP definition, State-of-the-Art, Grid Architectures, Example VPP studies, VPP Standards, and VPP Roadmap.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1984

Mesoscale and severe storms (Mass) data management and analysis system

Progress on the Mesoscale and Severe Storms (MASS) data management and analysis system is described. An interactive atmospheric data base management software package to convert four types of data (Sounding, Single Level, Grid, Image) into standard random access formats is implemented and integrated with the MASS AVE80 Series general purpose plotting and graphics display data analysis software package. An interactive analysis and display graphics software package (AVE80) to analyze large volumes of conventional and satellite derived meteorological data is enhanced to provide imaging/color graphics display utilizing color video hardware integrated into the MASS computer system. Local and remote smart-terminal capability is provided by installing APPLE III computer systems within individual scientist offices and integrated with the MASS system, thus providing color video display, graphics, and characters display of the four data types.

Hickey, J. S.↗

DOE OSTI2025

GreenThrift: Optimizing Carbon and Cost for Flexible Residential Loads

Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This paper highlights the potential conflict between electricity prices and carbon emissions and the resulting trade-offs in carbon-aware and cost-aware load scheduling. To address this trade-off, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2008

Low Cost Electric Propulsion Thruster for Deep Space Robotic Science Missions

Electric Propulsion (EP) has found widespread acceptance by commercial satellite providers for on-orbit station keeping due to the total life cycle cost advantages these systems offer. NASA has also sought to benefit from the use of EP for primary propulsion onboard the Deep Space-1 and DAWN spacecraft. These applications utilized EP systems based on gridded ion thrusters, which offer performance unequaled by other electric propulsion thrusters. Through the In-Space Propulsion Project, a lower cost thruster technology is currently under development designed to make electric propulsion intended for primary propulsion applications cost competitive with chemical propulsion systems. The basis for this new technology is a very reliable electric propulsion thruster called the Hall thruster. Hall thrusters, which have been flown by the Russians dating back to the 1970s, have been used by the Europeans on the SMART-1 lunar orbiter and currently employed by 15 other geostationary spacecraft. Since the inception of the Hall thruster, over 100 of these devices have been used with no known failures. This paper describes the latest accomplishments of a development task that seeks to improve Hall thruster technology by increasing its specific impulse, throttle-ability, and lifetime to make this type of electric propulsion thruster applicable to NASA deep space science missions. In addition to discussing recent progress on this task, this paper describes the performance and cost benefits projected to result from the use of advanced Hall thrusters for deep space science missions.

Manzella, David↗

NASA NTRS2015

Attitude Ground System (AGS) for the Magnetospheric Multi-Scale (MMS) Mission

MMS Overview Recall from Conrads presentation earlier today MMS launch: March 13, 2015 on an Atlas V from Space Launch Complex 40, Cape Canaveral, Florida MMS Observatory Separation: five minute intervals spinning at 3 rpm approximately 1.5 hours after launch MMS Science Goals: study magnetospheric plasma physics and understand the processes that cause power grids, communication disruptions and Aurora formation Mission: 4 identical spacecraft in tetrahedral formation with variable size1.2 x 12 RE in Phase 1, with apogee on dayside to observe bow shock1.2 x 25 RE in Phase 2, with apogee on night side to observe magneto tail Challenges Tight attitude control box, orbit and formation maintenance requirements Maneuvers on thrusters every two weeks Delta-H Spin axis direction and spin rate maintenance Delta-V Orbit and Formation maintenance Mission phase transitions AGS support Smart targeting prediction of Spin-Axis attitude in the presence of environmental torques to stay within the science attitude Determination of the spacecraft attitude and spin rate (sensitive to knowledge of inertia tensor)Calibrations to improve attitude determination results and improve orbit maneuvers Mass properties (Center of Mass, and inertia tensor for nutation and coning) Accelerometer bias (sensitive to the accuracy of the rate estimates) Sensor alignments.

Estimation and Calibration Support↗

DOE OSTI2024

Statistical Estimation of EV Driver Charging Behavior and Influential Factors

INL received data collected via telematics from battery electric vehicles (BEVs), and these vehicles were owned by retail customers who had entered into a telematics user agreement. The goal of analyzing these data was to develop mathematical models to characterize how different sets of BEV drivers use charging infrastructure at home and away from home (i.e., public charging) and quantify how various factors influence BEV drivers’ decision to charge and use available infrastructure. The data used in this analysis are unique because they provide real world BEV driving and charging behavior at the individual driving and parking event level. In this study we seek to leverage this data to quantify BEV charging and driving metrics to help inform models that predict quantities like the specific times when loads are imposed on the electrical grid due to BEV charging. Most models that have been developed to predict electrical grid load due to BEV charging, use simulations of BEV driving events and rely on assumptions such as every vehicle charges every night. Using a statistical modelling framework, we seek to investigate BEV charging behavior and quantitatively assess these common assumptions of BEV charging behavior.

33 - ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Spatial-Temporal PV Hosting Capacity Estimation and Evaluation

Evaluating Photovoltaic Hosting Capacity (PVHC) is an essential step in the process of integrating solar energy into power grids, particularly when focusing on the distribution network (DN) as the primary integration target. PVHC needs to be investigated, especially in cases where the grids are unbalanced, and their operational conditions vary spatially and temporally. This motivation prompted us to propose a scalable model tailored to this application. In this paper, we applied linearization to the alternating current optimal power flow (AC-OPF) and solar inverters, transforming the original problem into a mixed-integer linear programming (MILP) problem. Additionally, we accounted for the battery energy storage system (BESS) as a time-coupling factor for calculating PVHC. We then compared the PVHC results between the IEEE-13 bus and SMART-DS San Francisco (SFO) cases and discussed the extent to which BESS can enhance the PVHC of a DN. Furthermore, we designed a web-based graphical visualization for the SFO case, enabling user interaction with raw data and simulation results on a map through a graphical user interface (GUI). In summary, our results and findings provide valuable insights for future three-phase unbalanced AC-OPF PVHC practices and their visualization.

AC-optimal power flow↗

DOE OSTI2026

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Forecasting EV Charging Demand on the Distribution System

The U.S. transportation and electricity sectors have historically operated independently, but the growth of electric vehicles (EVs) is driving their convergence. After decades of stagnant demand, utilities must prepare for rising load growth, driven in part by transportation electrification. Utilities must anticipate when and where these new loads will materialize to effectively manage EV growth and maintain grid reliability. This presentation outlines NREL's approach to developing high-resolution EV load datasets for distribution planning, with insights from the Multi-State Transportation Electrification Impact Study on EV and load forecasting, infrastructure requirements, and managed charging strategies.

25 ENERGY STORAGE↗

NASA NTRS1992

Surface generation and editing operations applied to structural support of aerospace vehicle fuselages

The Solid Modeling Aerospace Research Tool (SMART) is a computer aided design tool used in aerospace vehicle design. Modeling of structural components using SMART includes the representation of the transverse or cross-wise elements of a vehicle's fuselage, ringframes, and bulkheads. Ringframes are placed along a vehicle's fuselage to provide structural support and maintain the shape of the fuselage. Bulkheads are also used to maintain shape, but are placed at locations where substantial structural support is required. Given a Bezier curve representation of a cross sectional cut through a vehicle's fuselage and/or an internal tank, this project produces a first-guess Bezier patch representation of a ringframe or bulkhead at the cross-sectional position. The grid produced is later used in the structural analysis of the vehicle. The graphical display of the generated patches allows the user to edit patch control points in real time. Constraints considered in the patch generation include maintaining 'square-like' patches and placement of longitudinal, or lengthwise along the fuselage, structural elements called longerons.

Schwartz, Susan K.↗

NASA NTRS2003

An Active Smart Material Control System for F/A-18 Buffet Alleviation

The vertical tail buffet problem of fighter aircraft occurs at high angles of attack when the vortical flow breaks down ahead of the vertical tails resulting in unsteady and unbalanced pressure loads on the vertical tails. The buffet loads imposed upon the vertical tails resulted in a premature fatigue failure of the tails, and consequently limits the performance and super maneuverability of twin-tail fighter aircraft. An active smart material control system using distributed piezoelectric actuators has been developed for buffet alleviation and is presented. The inboard and outboard surfaces of the vertical tail are equipped with piezoelectric actuators to control the buffet responses in the first bending and torsion modes. The electrodynamics of the piezoelectric actuators are expressed with a three-dimensional finite-element model. A single-input-single-output controller is designed to drive the active piezoelectric actuators. High-fidelity multidisciplinary analysis modules for the fluid dynamics, structure dynamics, electrodynamics of the piezoelectric actuators, control law, fluid structure interfacing, and grid motion are integrated into a multidisciplinary computing environment that controls the temporal synchronization of the analysis modules. At 30 degree angle of attack, RMS values of tip acceleration are reduced by as much as 12%. The peak values of the power spectral density of tail-tip acceleration are reduced by as much as 22% in the first bending mode and by as much as 82% in the first torsion mode. The actively controlled piezoelectric actuators were also effective in adding damping at wide range of angles of attack.

Sheta, Essam F.↗

DOE OSTI2024

Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications’ nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

The energy-saving potential of occupancy-centric smart thermostats has been extensively explored in simulations but lacked field testing for energy savings quantification and sensor performance assessment in real buildings. This paper presents a long-term field study conducted in a single-family home in Texas, U.S. to evaluate the performance of occupancy-centric controls (OCC) of HVAC (heating, ventilation, and air-conditioning) system in terms of energy savings, sensor accuracy, and impact on electric peak demand. The test site was equipped with a commercial off-the-shelf (COTS) smart thermostat and multiple occupancy presence sensors for OCC implementation. Additionally, a sub-metering system was installed to monitor electricity consumption of various end-use equipment, including the HVAC system. A supplementary device was installed to track the ground-truth occupancy for the accuracy evaluation of the occupancy presence sensor. Scenarios of baseline and OCC controls were alternated weekly over the 20-month testing period. The results indicated an effective OCC execution, as evidenced by indoor temperature profiles. During the 2023 cooling season, OCC achieved total energy savings of 1,958 kWh, corresponding to a 17.6% energy savings ratio. Under certain conditions, daily HVAC energy savings reached as high as 17 kWh, with a savings ratio of 35%. Sensor performance showed an overall accuracy of 83.8%, a False Positive Rate (FPR) of 12.8%, and a False Negative Rate (FNR) of 47.4%. A key limitation was the sensor’s inability to detect stationary occupants during sleep, leading to a midnight FNR of nearly 100% and significantly compromising thermal comfort. Additionally, the implementation of OCC resulted in extended periods of high electricity demand on summer afternoons, affecting occupant’s thermal comfort and posing potential challenges to community-level grid operations if OCC were widely adopted. Furthermore, this study addresses a critical research gap by empirically investigating energy-saving potential and occupancy sensor performance in residential buildings. Through a comprehensive field-testing study, the research examines the interrelationship between sensor accuracy, energy savings, and thermal comfort, an area that has received limited attention in the current literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗