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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2014

Progress in development of the neutron profile monitor for the large helical device

The neutron profile monitor stably operated at a high-count-rate for deuterium operations in the Large Helical Device has been developed to enhance the research on the fast-ion confinement. It is composed of a multichannel collimator, scintillation-detectors, and a field programmable gate array circuit. The entire neutron detector system was tested using an accelerator-based neutron generator. This system stably acquires the pulse data without any data loss at high-count-rate conditions up to 8 × 10{sup 5} counts per second.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An LSTM Deep Learning Network for ¿Background Radiation Prediction

Determination of appropriate background radiation is important in any measurement application. Environmental radiation monitors and monitors used to assess dose to individuals outside of controlled areas are particularly susceptible to changes in readings due to fluctuations in the environmental conditions. These fluctuations (e.g. radon progeny concentrations) lead to changes in the detector response in the actual radiation environment, and they need to be taken into account when extracting the net operational doses. Work has been ongoing to apply advances in Deep Learning and Artificial Intelligence to account for changes in detector responses based on environmental parameters; in particular, a Long-Short Term Memory (LSTM) Deep Learning architecture has been utilized to incorporate time-series data into a prediction model. In this work, we present the current status of the project to predict radiation measurements based on meteorological conditions and air packet trajectories extracted using the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model.

Degtiarenko, Pavel↗
DOE OSTI2014

NREL RSF Weather Data 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Weather data form the RSF systems energy model. The weather data was used to contextualize and compare energy use data like heating and cooling. The weather data was also used by the RSF Energy Model to model the energy use of the RSF. Energy Model data and Measured Energy data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗
DOE OSTI2014

NREL RSF Measured Data 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Measured Energy Data from the RSF Systems Model. The data includes a csv with measured data from NREL's Research and Support Facility (RSF) for 2011. The dataset contains hourly data for: Total Cooling (kW)- Total Heating (kW)- Total Mechanical (kW)- Total Lighting (kW)- Total Plug Loads (kW)- Total Data Center (kW)- Total Building (kW)- PV (kW)- Building Net (kW). Energy Model data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗
DOE OSTI2014

NREL RSF Energy Model 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Energy Model from the RSF Systems Model. Comparing actual performance (metered) with expected performance (modeled) is key to understand corrective actions to ensure performance as originally intended. The Energy Model in this submission was made to model energy usage in the RSF and was compared to actual metered data to verify the model. Measured data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗
DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1982

Isoelectric focusing in space

The potential of space electrophoresis is conditioned by the fact that all electrophoretic techniques require the suppression of gravity-caused convection. Isoelectric focusing (IEF) is a powerful variant of electrophoresis, in which amphoteric substances are separated in a pH gradient according to their isoelectric points. A new apparatus for large scale IEF, utilizing a recycling principle, has been developed. In the ground-based prototype, laminar flow is provided by a series of parallel filter elements. The operation of the apparatus is monitored by an automated array of pH and ultraviolet absorption sensors under control of a desk-top computer. The apparatus has proven to be useful for the purification of a variety of enzymes, snake venom proteins, peptide hormones, and other biologicals, including interferon produced by genetic engineering techniques. In planning for a possible space apparatus, a crucial question regarding electroosmosis needs to be addressed To solve this problem, simple focusing test modules are planned for inclusion in an early Shuttle flight.

Bier, M.↗
NASA NTRS2009

Software Considerations for Subscale Flight Testing of Experimental Control Laws

The NASA AirSTAR system has been designed to address the challenges associated with safe and efficient subscale flight testing of research control laws in adverse flight conditions. In this paper, software elements of this system are described, with an emphasis on components which allow for rapid prototyping and deployment of aircraft control laws. Through model-based design and automatic coding a common code-base is used for desktop analysis, piloted simulation and real-time flight control. The flight control system provides the ability to rapidly integrate and test multiple research control laws and to emulate component or sensor failures. Integrated integrity monitoring systems provide aircraft structural load protection, isolate the system from control algorithm failures, and monitor the health of telemetry streams. Finally, issues associated with software configuration management and code modularity are briefly discussed.

Murch, Austin M.↗
NASA NTRS2012

Alumina Based 500 C Electronic Packaging Systems and Future Development

NASA space and aeronautical missions for probing the inner solar planets as well as for in situ monitoring and control of next-generation aeronautical engines require high-temperature environment operable sensors and electronics. A 96% aluminum oxide and Au thick-film metallization based packaging system including chip-level packages, printed circuit board, and edge-connector is in development for high temperature SiC electronics. An electronic packaging system based on this material system was successfully tested and demonstrated with SiC electronics at 500 C for over 10,000 hours in laboratory conditions previously. In addition to the tests in laboratory environments, this packaging system has more recently been tested with a SiC junction field effect transistor (JFET) on low earth orbit through the NASA Materials on the International Space Station Experiment 7 (MISSE7). A SiC JFET with a packaging system composed of a 96% alumina chip-level package and an alumina printed circuit board mounted on a data acquisition circuit board was launched as a part of the MISSE7 suite to International Space Station via a Shuttle mission and tested on the orbit for eighteen months. A summary of results of tests in both laboratory and space environments will be presented. The future development of alumina based high temperature packaging using co-fired material systems for improved performance at high temperature and more feasible mass production will also be discussed.

Chen, Liang-Yu↗
NASA NTRS2021

Generating Flood Probability Map Based on Combined Use of Synthetic Aperture Radar and Optical Imagery

Despite a lot of efforts to respond flood hazards with remote sensing data, it is still difficult to generate an accurate flood map using solely optical or radar imagery. While optical data is relatively high-resolution and does not suffer from speckle noise compared to radar data, it is very likely to be impacted by cloud and shadow. On the other hand, radar imagery can be used in all weather conditions due to its capability of penetrating clouds. Although a significant improvement of flood monitoring capability is achieved by using radar data, it is still challenging to map urban floods because of strong backscattering by man-made structures. Therefore, complementary use of optical and radar imagery in flood response is required, particularly in urban areas. In this study, we have adopted the Bayesian Joint Probability function to combine two different flood products generated from SAR and optical imagery. Flood detection with SAR data relies on the difference of backscatter signals between standing water and rougher land surface, while a Normalized Difference Water Index (NDWI) approach is used for optical data. Specifically, Planet Dove data with its 3m spatial resolution is used with higher weight values to detect flood extent in urban areas.

floods↗
DOE OSTI2012

Caustic Recycling Pilot Unit to Separate Sodium from LLW at Hanford Site - 12279

As part of the Department of Energy (DOE) sponsored Advanced Remediation Technologies initiative, a scheme was developed to combine Continuous Sludge Leaching (CSL), Near-Tank Cesium Removal (NTCR), and Caustic Recycling Unit (CRU) using Ceramatec technology, into a single system known as the Pilot Near-Tank Treatment System (PNTTS). The Cesium (Cs) decontaminated effluent from the NTCR process will be sent to the caustic recycle process for recovery of the caustic which will be reused in another cycle of caustic leaching in the CSL process. Such an integrated mobile technology demonstration will give DOE the option to insert this process for sodium management at various sites in Hanford, and will minimize the addition of further sodium into the waste tanks. This allows for recycling of the caustic used to remove aluminum during sludge washing as a pretreatment step in the vitrification of radioactive waste which will decrease the Low Level Waste (LLW) volume by as much as 39%. The CRU pilot process was designed to recycle sodium in the form of pure sodium hydroxide. The basis for the design of the 1/4 scale pilot caustic recycling unit was to demonstrate the efficient operation of a larger scale system to recycle caustic from the NTCR effluent stream from the Parsons process. The CRU was designed to process 0.28 liter/minute of NTCR effluent, and generate 10 M concentration of 'usable' sodium hydroxide. The proposed process operates at 40 deg. C to provide additional aluminum solubility and then recover the sodium hydroxide to the point where the aluminum is saturated at 40 deg. C. A system was developed to safely separate and vent the gases generated during operation of the CRU with the production of 10 M sodium hydroxide. Caustic was produced at a rate between 1.9 to 9.3 kg/hr. The CRU was located inside an ISO container to allow for moving of the unit close to tank locations to process the LLW stream. Actual tests were conducted with the NTCR effluent simulant from the Parsons process in the CRU. The modular CRU is easily scalable as a standalone system for caustic recycling, or for NTTS integration or for use as an In-Tank Treatment System to process sodium bearing waste to meet LLW processing needs at the Hanford site. The standalone pilot operation of the CRU to recycle sodium from NTCR effluent places the technology demonstration at TRL level 6. Multiple operations were performed with the CRU to process up to 500 gallons of the NTCR effluent and demonstrate an efficient separation of up to 70 % of the sodium without solids precipitation while producing 10 M caustic. Batch mode operation was conducted to study the effects of chemistry variation, establish the processing rate, and optimize the process operating conditions to recycle caustic from the NTCR effluent. The performance of the CRU was monitored by tracking the density parameter to control the concentration of caustic produced. Different levels of sodium were separated in tests from the effluent at a fixed operating current density and temperature. The voltage of the modules remained stable during the unit operation which demonstrated steady operation to separate sodium from the NTCR effluent. The sodium transfer current efficiency was measured in testing based on the concentration of caustic produced. Measurements showed a current efficiency of 99.8% for sodium transfer from the NTCR effluent to make sodium hydroxide. The sodium and hydroxide contents of the anolyte (NTCR feed) and catholyte (caustic product) were measured before and after each batch test. In two separate batch tests, samples were taken at different levels of sodium separation and analyzed to determine the stability of the NTCR effluent after sodium separation. The stability characteristics and changes in physical and chemical properties of the NTCR effluent chemistry after separation of sodium hydroxide as a function of storage time were evaluated. Parameters such as level of precipitated alumina, total alkalinity, analysis of Al, Na, K, Cs, Fe, OH, nitrate, nitrite, total dissolved and undissolved solids, viscosity, density, and other parameters of the NTCR effluent were measured. Changes in rheology and properties of NTCR stream to support downstream handling of the effluent after sodium separation was the basis for the analysis. The results show that the NTCR effluent is stable without the precipitation of aluminum hydroxide after 70% of the sodium was separated from the effluent. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

How systems engineering and human factors can assist SMR vendors with the licensing process - 345

There is growing interest in the development of small modular reactors (SMRs) as a cost-effective renewable source of energy. Most of the current conceptual designs propose advanced and automated control and monitoring technologies which would require complex human system interfaces. The proposals also include a reduction in the number of staff, and partial or full remote control of the facilities. The human factors challenges of such a design philosophy range from identification of information requirements of users to assessing workload to deciding on best information visualization techniques to ensure situation awareness is maintained in a remote control facility. Addressing these challenges will require a systems engineering approach where user requirements are identified through a systematic approach for various contexts, including normal, abnormal, and accident conditions. A systems engineering process will integrate human factors as well as other specialty disciplines such as reliability and design optimization in a coherent fashion that would lead to realization of a successful system that addresses user requirements. The licensing model in Canada is based upon a graded approach and is largely technology neutral. A key factor in ensuring effective licensing effort is identification of regulatory requirements. This paper discusses how adopting a systems engineering approach and early consideration of human factors principles and requirements will not only ensure an efficient design process, but will also help with developing a design that meets the regulatory requirements. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Application and development of technologies for engine-condition-based maintenance of emergency diesel generators

The emergency diesel generator (EDG) of a nuclear power plant has the role of supplying emergency electric power to protect the reactor core system in the event of the loss of offsite power supply. Therefore, EDGs should be subject to periodic surveillance testing to verify their ability to supply specified frequencies and voltages at design power levels within a limited time. To maintain optimal reliability of EDGs, condition monitoring/diagnosis technologies must be developed. Changing from periodic disassemble maintenance to condition-based maintenance (CBM) according to predictions of equipment condition is recommended. In this paper, the development of diagnosis technology for CBM and the application of a diesel engine condition-analysis system are described. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗