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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A labeled dataset for building HVAC systems operating in faulted and fault-free states

Abstract Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Energy Systems Integration Facility Stewardship Summary: Fiscal Year 2023

A summary of NREL's good stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, infrastructure and capability upgrades, and examples of R&D impact. In fiscal year 2023, ESIF installed its third-generation high-performance computer, expanded capabailities for validating building energy controls, and demonstrated a leap in scale through virtual networking with another national laboratory. ESIF researchers made breakthroughs in cybersecurity for energy systems, efficient high-powered electric vehicle charging, leveraging reinforcement learning for grid resilience, and more.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
NASA NTRS1989

Real-time graphics for the Space Station Freedom cupola, developed in the Systems Engineering Simulator

Among the Lyndon B. Johnson Space Center's responsibilities for Space Station Freedom is the cupola. Attached to the resource node, the cupola is a windowed structure that will serve as the space station's secondary control center. From the cupola, operations involving the mobile service center and orbital maneuvering vehicle will be conducted. The Systems Engineering Simulator (SES), located in building 16, activated a real-time man-in-the-loop cupola simulator in November 1987. The SES cupola is an engineering tool with the flexibility to evolve in both hardware and software as the final cupola design matures. Two workstations are simulated with closed-circuit television monitors, rotational and translational hand controllers, programmable display pushbuttons, and graphics display with trackball and keyboard. The displays and controls of the SES cupola are driven by a Silicon Graphics Integrated Raster Imaging System (IRIS) 4D/70 GT computer. Through the use of an interactive display builder program, SES, cupola display pages consisting of two dimensional and three dimensional graphics are constructed. These display pages interact with the SES via the IRIS real-time graphics interface. The focus is on the real-time graphics interface applications software developed on the IRIS.

Red, Michael T.↗
DOE OSTI2021

Huslia Tribal Council Biomass Project: A Project to Increase Sustainability and Reduce Energy Costs in Huslia, AK

The Huslia Tribal Council (HTC) and the Tanana Chiefs Conference (TCC), partnering with the City of Huslia, the Yukon-Koyukuk School District, and Alaska Native Tribal Health Consortium, applied to the U.S. Department of Energy Office of Indian Energy for funding of a community biomass boiler system. The biomass system contributes to heating three community buildings in Huslia, Alaska with locally harvested fuels: the clinic, the water treatment plant/washeteria, and the school. The remaining heating needs are supported by in-building oil-fired boilers, burning imported heating fuel. Some issues, such as control set points on the oil boilers, need to be resolved before the biomass heating system is used at full capacity. The project objectives of the community biomass boiler system were cost reduction, carbon reduction, community resilience, and economic development. Once fully operational, the cost reduction from decreased fuel oil usage due to support from the biomass boiler system is expected to more than offset the cost of purchasing locally harvested biofuel, resulting in overall savings to the community. Locally sourced wood is considered carbon-neutral, so the biomass boiler system decreases the carbon footprint of heating the community buildings. The project increases resilience by decreasing dependence on heating oil brought in from elsewhere. Lastly, it produces economic development by employing locals to gather the wood burned in the biomass boiler. This project satisfies all of the objectives that HTC set out to achieve.

09 BIOMASS FUELS↗
DOE OSTI2020

Development and Evaluation of Occupancy-Aware Model Predictive Control for Residential Building Energy Efficiency and Occupant Comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Catalyst Layer Design, Manufacturing and In-line Quality Control

In this project we successfully demonstrated the capabilities of the Reactive Spray Deposition Technology (RSDT) to fabricate large-scale CCMs for advanced PEMWEs that have one-order of magnitude lower PGM loading in their catalyst layers, and performance comparable with the commercial state-of-the-art CCMs. The RSDT is a unique methodology that combines the catalyst synthesis and CCM fabrication in one step and reduces dramatically the time for CCM manufacturing. As fabricated large-scale CCMs with geometric area of 680 cm2 demonstrated excellent activity and durability performance, and the novel duo-recombination layer design paves the way for solving the safety concerns related to PEMWEs. In addition, excellent activity and durability performance has been demonstrated with RSDT fabricated CCMs with thinner membranes and duo RL design. This is a novel approach for further performance improvement of the MEAs for PEMWEs that has been successfully demonstrated for the first time in this project. The integration of the in-situ laser diagnostics system along with the in-line optical quality control system within the RSDT that has been achieved and demonstrated in this project, is an example for possibility of designing and building advanced manufacturing technologies that can meet the requirements of the future manufacturing. Therefore, the RSDT offers a precise real-time monitoring and control of the particles size, composition, loading, porosity, thickness, and defects in the catalysts’ layers, which render this technology as the best candidate for manufacturing of cost effective CCMs for PEMWEs. By using RSDT we successfully met all project’s milestones, Go/No-Go decision, objectives, goals, and deliverables.

08 HYDROGEN↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2025

Light-Modulated Self-Assembly of Synthetic Nanotubes

Artificial biomolecular polymers with the capacity to respond to stimuli are emerging as a key component to the development of living materials and synthetic cells. Here, in this work, we demonstrate artificial DNA tubular nanostructures that form in response to light in a dose-dependent manner. These nanotubes assemble from programmable DNA tile motifs that are engineered to include a UV-responsive domain so that UV irradiation activates nanotube self-assembly. We demonstrate that the nanotube formation speed can be tuned by adjusting the UV dose. We then couple the light-dependent activation of tiles with RNA transcription, making it possible to control nanotube formation via concurrent physical and biochemical stimuli. Finally, we illustrate how UV activation effectively controls nanotube assembly in confinement as a rudimentary stimulus-responsive cytoskeletal system that can achieve various conformations in a minimal synthetic cell. This study contributes new tile designs that are immediately useful to building biomolecular scaffolds with controllable dynamics in response to multiple stimuli.

DNA nanotechnology↗
NASA NTRS2017

Launch Control Network Engineer

The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.

Medeiros, Samantha↗
DOE OSTI2023

Stochastic scheduling for commercial building cooling systems: considering uncertainty in zone temperature prediction

Here, this paper presents the first attempt to address the uncertainty in zone temperature prediction with stochastic optimization. The uncertain zone temperature is a process uncertainty and has not been considered in the existing stochastic optimization for building control. To fill this gap, we proposed a novel formulation of stochastic optimization to handle process uncertainty in building control. Specifically, we first examined the accuracy of a typical linear model for predicting zone temperature. We then formulated the scheduling of the building cooling system as a stochastic optimization problem over a 24-hour look-ahead period to minimize the electricity cost of the studied building cooling system. After that, we applied the proposed stochastic load scheduling (SLS) to a direct expansion (DX) cooling system that serves a medium office building. Through simulation with a detailed building energy simulation software, EnergyPlus, we evaluated the operational cost and the thermal comfort compared with a deterministic load scheduling. The operation cost of scheduling was found to vary with the level of zone temperature prediction uncertainty. The proposed SLS can mitigate the impacts of uncertain zone temperature predictions on both operational cost and thermal comfort. The evaluation results indicate that the proposed SLS works better when the uncertainty level is more significant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Thermally Anisotropic Composites for Heat Redirection and Thermal Management in Building Envelopes

In building envelope systems, thermal management is important from energy conservation and thermal comfort perspectives, and is typically done via insulation, thermal mass, solar control and shading, etc. Thermal anisotropy is commonly utilized in electronics for heat dissipation. This article investigates the feasibility of utilizing thermally anisotropic composites (TACs) to reduce unwanted heat gains and losses through the building envelope by redirecting the heat transfer to or from appropriate heat sinks and sources.Numerical simulations of building envelope components using COMSOL Multiphysics and whole building simulations using EnergyPlus (E+) were performed to estimate the energy savings potential of TACs used in external walls of residential buildings. Application of TACs was compared with baseline walls with cavity insulation as well as with and without exterior continuous insulation (CI). The COMSOL simulations indicated 75% or higher reductions in heat gains when using a TAC coupled with a heat sink compared to an exterior CI case. In terms of energy savings in a hot climate, E+ models estimated cooling energy savings of 19% with a "TAC + heat sink" compared to a baseline wall with only cavity insulation. Further, a small-scale test sample with a TAC-heat sink system was built and tested in a multi-transducer heat flow meter (HFM). The heat sink was a copper tube circulating cold water. The experiments verified the re-direction of heat to a sink and significant reduction in heat transfer through the test specimen. The experiments showed that the heat redirection only occurred in the presence of both thermal anisotropy and water circulation; if either element was missing, the system behaved like a traditional envelope component.

Biswas, Kaushik↗
DOE OSTI2021

Modelling of Wastewater Heat Recovery Heat Pump Systems

Wastewater heat recovery is currently an underutilized technology that could be part of solving the climate crisis. A large portion of the heat that leaves a building in the form of wastewater is potentially recoverable for pre-heating domestic hot water or other service water systems. While there are several different approaches to wastewater heat recovery, this project focused on creating detailed, integrated building models for wastewater heat recovery heat pump systems. EnergyPlus models were developed featuring inputs and assumptions corresponding to manufacturers’ specifications, performance lab test data and feedback from engineering consultants. EnergyPlus’s supervisory control Energy Management System objects were heavily relied upon to overcome modelling challenges. The developed EnergyPlus model was integrated into U.S. Department of Energy New Construction Reference Building models for various climate zones and building types to assess potential energy use, energy cost and greenhouse gas emission reductions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2021

Part II: FY20 CIF Annual Report - LAPS: Lunar Autonomous Positioning System

This project concerns construction of an orbital and ground resource network that provides Position, Navigation, and Timing (PNT) services for lunar surface operations. While functionally similar to Global Navigation Satellite Systems (GNSS) for Earth, this system will instead build an automated PNT framework utilizing limited infrastructure on board orbiting assets along with a controlled number of highly accurate assets, or anchor nodes. The goal is to use advanced algorithms to autonomously coordinate on demand or as needed asset participation to achieve orbit determination and time synchronization of accuracy sufficient for end-user localization. Other proposed lunar PNT solutions, such as weak signal GPS, will not meet many mission localization requirements without additional user INS augmentation and a dedicated GNSS constellation would require prohibitive infrastructure development. This proposed effort, the Lunar Autonomous Positioning System (LAPS), instead offers a design that could be deployed in the near term and is facilitated by accessible hardware technology.

Kelley Hashemi↗
DOE OSTI2020

Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems

With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Dynamic Modeling, Trajectory Optimization, and Linear Control of Cable-Driven Parallel Robots for Automated Panelized Building Retrofits

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment.

CDPR↗
NASA NTRS2012

Variable-Speed Simulation of a Dual-Clutch Gearbox Tiltrotor Driveline

This investigation explores the variable-speed operation and shift response of a prototypical two-speed dual-clutch transmission tiltrotor driveline in forward flight. Here, a Comprehensive Variable-Speed Rotorcraft Propulsion System Modeling (CVSRPM) tool developed under a NASA funded NRA program is utilized to simulate the drive system dynamics. In this study, a sequential shifting control strategy is analyzed under a steady forward cruise condition. This investigation attempts to build upon previous variable-speed rotorcraft propulsion studies by 1) including a fully nonlinear transient gas-turbine engine model, 2) including clutch stick-slip friction effects, 3) including shaft flexibility, 4) incorporating a basic flight dynamics model to account for interactions with the flight control system. Through exploring the interactions between the various subsystems, this analysis provides important insights into the continuing development of variable-speed rotorcraft propulsion systems.

DeSmidt, Hans↗
DOE OSTI2020

Power Grid Simulation Testbed for Transactive Energy Management Systems

To effectively engage demand-side and distributed energy resources (DERs) for dynamically maintaining the electric power balance, the challenges of controlling and coordinating building equipment and DERs on a large scale must be overcome. Although several control techniques have been proposed in the literature, a significant obstacle to applying these techniques in practice is having access to an effective testing platform. Performing tests at scale using real equipment is impractical, so simulation offers the only viable route to developmental testing at scales of practical interest. Existing power-grid testbeds are unable to model individual residential end-use devices for developing detailed control formulations for responsive loads and DERs. Furthermore, they cannot simulate the control and communications at subminute timescales. To address these issues, this paper presents a novel power-grid simulation testbed for transactive energy management systems. Detailed models of primary home appliances (e.g., heating and cooling systems, water heaters, photovoltaic panels, energy storage systems) are provided to simulate realistic load behaviors in response to environmental parameters and control commands. The proposed testbed incorporates software as it will be deployed, and enables deployable software to interact with various building equipment models for end-to-end performance evaluation at scale.

24 POWER TRANSMISSION AND DISTRIBUTION↗