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At least 757 records · Page 42

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NASA NTRSDate not supplied

Simplified Applied Loads of a Transonic Truss-Braced Wing Derived from Computational Fluid Dynamics

The transonic truss-braced wing (TTBW) concept is under consideration by the NASA Advanced Air Transport Technology (AATT) project due to its potential to reduce fuel consumption of transport aircraft. To properly evaluate the TTBW concept, detailed structural modeling and subsequent test planning is underway, which requires a simplified set of discrete applied loads that simulate the distributed aerodynamic pressures. Given the unique architecture of the TTBW and its intended operation in the transonic regime, computational fluid dynamics (CFD) is the ideal approach to employ to ensure the aerodynamics of this configuration are sufficiently captured. To convert aerodynamic results into input for structural models, application of a data transfer tool, Discrete Data Transfer Between Dissimilar Meshes (DDTBDM), is presented to resolve aerodynamic pressures from CFD into single load vectors at hardpoints along both the wing and the strut. Evaluation of mapping the aerodynamic forces to hardpoints at rib and spar intersections in a structural model is performed, and the results are also compared with a linear static aeroelastic analysis. The structural responses indicate that distributing forces along only the wing and strut forward spars is insufficient for simulating the distributed aerodynamic loads. Adding moments along the forward spars improved the global response of the wing, but the point moments created unrealistic local rotations within the structure. When distributing loads along both the forward and aft spars of the wing and strut, the global response was less sensitive to the inclusion of point moments, which indicated the arrangement of point forces alone was sufficient input for structural design work and testing and provided a more continuous loading profile.

Finite Element↗

NASA NTRSDate not supplied

Simplified Applied Loads of a Transonic Truss-Braced Wing Derived from Computational Fluid Dynamic

Finite Element↗

NASA NTRSDate not supplied

Space Shuttle Orbiter-External Tank Forward Bipod Loads Analysis

The forward bipod system provides a robust attachment between the Space Shuttle Orbiter and the External Tank. Closed-form analyses were performed to determine how much additional load-carrying capability exists in this system beyond the current certified load limits. Results of these analyses suggest that the forward bipod system is capable of carrying loads that are at least 25 percent greater than the current flight limit loads, and possibly even more if lower safety factors are allowed. The forward bipod system may also have additional load-carrying capabilities, given the conservative nature of these analyses.

Space Shuttle↗

DOE OSTI2024

Energy characteristics of multi-chiller load distribution algorithms in a large office building

Here, this study evaluates the energy efficiency of multi-chiller systems in large office buildings, focusing on their optimization across various climate zones as defined by ASHRAE. Using EnergyPlus for simulations, the research examines five different load distribution algorithms in multi-chiller systems that range from one to ten chillers, aiming to understand their effectiveness in 15 distinct climate zones. The primary objectives of the study include identifying the energy efficiency of multi-chiller systems in each climate zone, determining the appropriate number of chillers for each zone, and evaluating the performance of the load distribution algorithms. Based on the U.S. Department of Energy’s commercial building model, the results suggest that multi-chiller systems can significantly reduce cooling energy consumption in various climates. Among the algorithms evaluated, the Sequential Uniform Part Load Ratio (SUPLR) algorithm demonstrates notable efficiency, especially in the 4A climate zone (Baltimore), where it achieves substantial energy savings. Applying the SUPLR algorithm in a multi-chiller setup with four chillers in this zone leads to an estimated 24.5 % reduction in energy usage, equivalent to 183 MW annually. The research indicates that a range of 3 to 5 chillers is typically optimal for most climate zones. In-depth analysis in the 4A climate zone highlights the importance of minimizing operation hours at low Part Load Ratios (PLR) to ensure that chillers operate at a high Coefficient of Performance (COP). This strategy underscores the potential of well-designed multi-chiller systems to reduce cooling energy demand, particularly in climates with transitional seasons. This study provides an overview of the energy-saving potential of multi-chiller systems, applicable across a variety of climatic scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Methodology and Development of SLS Liftoff Loads

The methodologies and development of the loads experienced during liftoff of the NASA SLS Block I vehicle will be presented in this paper. The liftoff loads analysis traditionally captures the dynamics of vehicle/pad separation after engine/booster ignition coupled with other launch day environments like wind, overpressure, and engine side loads. Additionally, the liftoff analysis includes an on-pad engine shutdown prior to booster ignition. The SLS liftoff analysis is performed using a Monte Carlo analysis and this paper will explore the Monte Carlo process and the statistical approach to calculating the limit loads.

SLS↗

DOE OSTI2024

Paths Forward: Approaches to Achieve Plug and Process Load Efficiency and Control in Commercial Buildings: Preprint

To accomplish net-zero carbon in the built environment by 2050, we must equitably decarbonize commercial buildings, which includes reducing plug and process loads (PPLs). PPLs are plug-in or hardwired electric and gas loads that are not directly associated with major building end uses like lighting and heating, ventilating, and air conditioning. PPLs account for a growing portion of U.S. commercial building energy consumption. Although commercial building PPL strategies and technologies are available today, they have not been adopted at a level sufficient to achieve significant savings and load flexibility across the building stock. In our "Pathways to Plug and Process Load Efficiency and Control" study, we investigated why these technologies and strategies have not seen widespread adoption and identified five behavior and technology pathways to increase PPL reduction in commercial buildings. In this paper, we expand beyond identifying the pathways and discuss approaches for achieving them. We discuss the importance of collecting and sharing data and case studies on PPL energy consumption and savings from control technology implementation, including code-required measures, for increasing adoption. Centralizing case studies and data, engaging industry organizations, and promoting awareness of PPL efficiency benefits to relevant groups are also key approaches. Additionally, funding, incentives, and rebate programs play important roles in driving PPL efficiency and control adoption. Finally, we discuss integrating PPL efficiency into broader company goals, such as environmental, social and governance (ESG) strategies and green building certifications, to further drive adoption.

adoption pathways↗

DOE OSTI2026

Categorizing Plug Load Solutions by Ease of Implementation

Plug loads account for a growing share of U.S. commercial building electricity use, projected to rise from 16% today to 21% by 2050. Managing these loads presents a substantial opportunity for reducing energy costs while providing additional operational benefits, such as improved asset management and occupant comfort. Despite their potential, plug loads are numerous, diverse, and highly occupant-dependent, making control challenging. This publication organizes plug load control strategies by level of effort, offering building owners and operators a staged approach to implement interventions ranging from smart outlets and automatic receptacle controls to behavioral strategies. By following these actionable steps, building stakeholders can reduce energy consumption, lower utility bills, and realize broader operational advantages.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗

DOE OSTI2024

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. Here, this study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

14 SOLAR ENERGY↗

DOE OSTI2024

In situ measurement of three-dimensional intergranular stress localizations and grain yielding under elastoplastic axial-torsional loading

The three-dimensional grain-averaged response of solid bar samples under non-proportional (NP) elastoplastic axial-torsional loading was investigated using in situ high energy diffraction microscopy (HEDM) and companion crystal plasticity finite element (CPFE) modeling. Important stress metrics including applied shear (σ θZ ) and axial (σ ZZ ) stress tensor components, stress and stress deviator tensor invariants (I 1 , J 2 , and J 3 ), von Mises equivalent stress (σ$^{grain}_{VM}$), maximum resolved shear stress (mRSS), stress triaxiality (η), and lode angle parameter ($\barθ$) values were tracked for ~300 grains under two different loading conditions: (1) Torsion-dominated loading (low NP) and (2) Tension-torsion loading (high NP) in equiatomic NiCoCr, a representative multicomponent face-centered cubic (FCC) superalloy. Overall, significant stress localizations existed within both samples as evidenced by the radial dependence of grain-resolved σ θZ , σ$^{grain}_{VM}$, and J 2 ; by comparison, I 1 , J 3 , η, and $\barθ$ metrics did not show discernible trends within the volume. These stress localizations reveal a complex interplay between axial and shear stress components (e.g., stress coupling) resulting in grain yielding near the sample surface largely driven by shear stress, whereas internal grain yielding was largely accommodated by axial stress. Grain-resolved stress localization trends were described well by the CPFE model, although some discrepancies in magnitude occurred, particularly for volumetric stress metrics (I 1 and η) due to initial type II residual stress distributions. The superposition of initial residual stress states onto CPFE grain-resolved data significantly improved model accuracy for η. This suggests that residual stresses more strongly influence the simulation of volumetric rather than deviatoric (yield) stress metrics.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Characterizing electrical panel capacity, breaker space, and loads in U.S. single-family homes

Electrical panels distribute electricity to appliances, equipment, and other building loads and maintain safety by preventing overloads of branch circuits, feeders, and utility service wires connecting homes to the grid. Load calculations in the National Electrical Code (NEC) determine the capacity of installed electrical panels in the United States, which are rated in amps (A). Circuit breakers within electrical panels affect the magnitude and number of loads that panels can serve. In home retrofits and renovations, existing electrical panels may not accommodate new electrical loads, either due to insufficient electrical capacity or a lack of available breaker spaces. Homes with these panel constraints may need to replace existing panels with new panels that have greater capacity and breaker space.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Implementation of Advanced Grid Support Functionalities by Smart Operation of Residential Loads with low Cost Converter Interface

This paper investigates a grid-supportive load concept for small-scale residential appliances, focusing on a residential refrigerator. Power consumption is adjusted based on grid conditions to achieve IEEE-1547 grid support functions. Two key aspects are presented: a low-cost refrigerator converter with Lyapunov energy function-based local controllers for speed control, and the impact on a standard microgrid system, demonstrating advanced grid support from the load side. This method enhances grid resilience and reliability and can be extended to other residential loads. The study contributes to efficient and robust grid-supportive load management systems, showing promising performance. This approach has the potential to improve overall grid stability and can be adapted for various types of residential appliances. The modeling and simulations in MATLAB/Simulink and PLECS confirm the feasibility and effectiveness of the proposed solution. Future work will explore real-world implementation and scalability of this concept for broader applications.

grid supportive loads (GSL)↗

DOE OSTI2025

Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks

Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.

electric load forecasting↗

DOE OSTI2025

ComStock Measure Documentation: Ideal Thermal Air Loads

This study provides hypothetical thermal heating and cooling loads for ComStock models representing the U.S. commercial building stock. This measure scenario removes all HVAC models from the baseline ComStock building model and instead uses "ideal air" to meet loads. "Ideal air" can be thought of as an HVAC unit that mixes air at the zone exhaust condition with the specified amount of outdoor air, and then adds or removes heat and moisture at 100% efficiency to produce a supply air stream at the specified conditions. The resulting ideal thermal loads are represented under the "district" fuel type for both heating and cooling and can be found in both annual and timeseries results in the ComStock public dataset. This measure scenario does not represent any real technology or improvement, but rather, serves as a resource for thermal heating and cooling loads for buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Large Load Management and Grid Planning

This presentation will cover these topics, followed by a discussion and Q&A: Grid impacts of load growth of the current bulk power system, a review of recent trends and events related to large load integration, what might these new large loads look like, what are options for reducing costs and timelines of integration, and working toward a framework for identifying hot spots for efficiently interconnecting large loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Advanced Load Forecasting

This presentation presents information about electric utility load forecasting in the U.S. It provides an overview of load forecasting and describes the current state of the industry. Current load forecasting challenges, opportunities, and interests are presented, including feedback from a 2024 workshop on Integrated Distribution System Planning. The presentation also describes a variety of NREL tools and capabilities that support utility load forecasting efforts. This was presented as part of NREL's Utility Planning Resources for Energy Transition Webinar Series.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

ComStock Measure Documentation: Lighting Control for Load Shedding

This report describes the modeling methodology for a single end-use savings shape measure - lighting control for load shedding - and briefly introduces key results. The lighting control for load shedding measure applies lighting dimming control to reduce the lighting load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then adjusts the lighting dimming level by a percentage reduction from the original schedules during the peak window to reduce the peak demand. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 2%-7% daily peak demand reduction performance for applicable buildings, and 0.43% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗