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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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226 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Evaluation of energy savings potential of variable refrigerant flow (VRF) from variable air volume (VAV) in the U.S. climate locations

Variable refrigerant flow (VRF) systems are known for their high energy performance and thus can improve energy efficiency both in residential and commercial buildings. The energy savings potential of this system has been demonstrated in several studies by comparing the system performance with conventional HVAC systems such as rooftop variable air volume systems (RTU-VAV) and central chiller and boiler systems. This paper evaluates the performance of VRF and RTU-VAV systems in a simulation environment using widely-accepted whole building energy modeling software, EnergyPlus. A medium office prototype building model, developed by the U.S. Department of Energy (DOE), is used to assess the performance of VRF and RTU-VAV systems. Each system is placed in 16 different locations, representing all U.S. climate zones, to evaluate the performance variations. Both models are compliant with the minimum energy code requirements prescribed in ASHRAE standard 90.1-2010 — energy standard for buildings except low-rise residential buildings. Finally, a comparison study between the simulation results of VRF and RTU-VAV models is made to demonstrate energy savings potential of VRF systems. The simulation results show that the VRF systems would save around 15–42% and 18–33% for HVAC site and source energy uses compared to the RTU-VAV systems. In addition, calculated results for annual HVAC cost savings point out that hot and mild climates show higher percentage cost savings for the VRF systems than cold climates mainly due to the differences in electricity and gas use for heating sources.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

CALPHAD Uncertainty Quantification and TDBX

CALPHAD uncertainty quantification (UQ) is the foundation of materials design with quantified confidence. We report a framework and software packages to enable CALPHAD UQ assessment and calculation using commercial CALPHAD software (Thermo-Calc). This Bayesian inference framework is coupled with a Markov chain Monte Carlo algorithm to establish uncertainty traces with a given thermodynamic database file (TDB) and corresponding experimental data points. This general framework is demonstrated with the Ni–Cr binary system. The algorithm is firstly validated on synthetic data with known ground truth. Then it is applied to real experimental data to generate posterior traces. We develop a file format named TDBX, which provides a single source of truth by combining the original TDB content and the traces for each assessed Gibbs energy parameter. CALPHAD UQ calculations are performed based on the TDBX file, from which uncertainties for phase boundaries, enthalpy curves, and solidification range are collected as examples of basic design parameters. This TDBX file with corresponding scripts are made open-source. Finally, the combination of CALPHAD UQ assessments and calculations connected by TDBX supports uncertainty-assisted modeling, enabling the integrated application of modern design with uncertainty methodologies to computational materials design.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Graphics Processing Unit Assisted Thermographic Compositing

Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often great, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques. Technical Methodology/Approach: Apply massively parallel algorithms and data structures to the specific analysis requirements presented when working with thermographic data sets.

Ragasa, Scott↗
NASA NTRS1997

Analyzing Tabular and State-Transition Requirements Specifications in PVS

We describe PVS's capabilities for representing tabular specifications of the kind advocated by Parnas and others, and show how PVS's Type Correctness Conditions (TCCs) are used to ensure certain well-formedness properties. We then show how these and other capabilities of PVS can be used to represent the AND/OR tables of Leveson and the Decision Tables of Sherry, and we demonstrate how PVS's TCCs can expose and help isolate errors in the latter. We extend this approach to represent the mode transition tables of the Software Cost Reduction (SCR) method in an attractive manner. We show how PVS can check these tables for well-formedness, and how PVS's model checking capabilities can be used to verify invariants and reachability properties of SCR requirements specifications, and inclusion relations between the behaviors of different specifications. These examples demonstrate how several capabilities of the PVS language and verification system can be used in combination to provide customized support for specific methodologies for documenting and analyzing requirements. Because they use only the standard capabilities of PVS, users can adapt and extend these customizations to suit their own needs. Those developing dedicated tools for individual methodologies may find these constructions in PVS helpful for prototyping purposes, or as a useful adjunct to a dedicated tool when the capabilities of a full theorem prover are required. The examples also illustrate the power and utility of an integrated general-purpose system such as PVS. For example, there was no need to adapt or extend the PVS model checker to make it work with SCR specifications described using the PVS TABLE construct: the model checker is applicable to any transition relation, independently of the PVS language constructs used in its definition.

Owre, Sam↗
DOE OSTI2015

Performance analysis of data intensive cloud systems based on data management and replication: a survey

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobiledevices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Malik, Saif Ur Rehman↗
DOE OSTI2019

Scaling Input Distributions for Probabilistic Models - 19472

It is important to address scaling issues when building models to support risk-based decisions for radioactive waste disposal sites. Currently, probabilistic risk assessment and probabilistic performance assessment models are run for multiple realizations where each realization uses one value from each input distribution over the entire modeled time. The input distributions represent the current state of knowledge in the mean of the input variable over the spatial region of interest and over the time period of the model run. This paper focuses on the time factor in input distribution scaling because it presents more serious conceptual and distributional challenges than the spatial factor. One of the challenges in temporal scaling is the effect of non-linearity on propagation of distribution uncertainty through to uncertainty in results. Radioactive waste disposal models tend to have both linear and non-linear components in their models. Dose assessment calculations are typically represented by linear models, while contaminant transport models have linear and non-linear components. In principle, temporal scaling of input parameter distributions for a linear model is straightforward, but scaling of input distributions for non-linear models is not. This paper uses examples to investigate the effects of input distribution temporal scaling on simple linear and non-linear models. Different scaling scenarios were investigated by comparing three analysis tools: analytical mathematical representations, statistical software programming, and GoldSim Monte Carlo simulation software. R statistical software is an open source statistical programming language often used for statistical analyses and graphical representation of data. GoldSim is a robust probabilistic dynamic modeling platform used in performance assessment modeling of radioactive waste sites. A simple linear model, a quadratic model and a multiplicative model were the cases explored for each of these tools. For the three analysis tools considered and with each of the scaling cases, 1,000 realizations were implemented from distributions of the random variables of interest. This is a sufficient number of realizations for simple models to capture the model space. An annual temporal resolution of a 100-year simulation period was chosen for all models and analysis tools. The effects of temporal scaling were demonstrated by investigating two different options for time scaling. Option 1 was set up so that the input distributions represented the expected value on an annual time scale, and a new value from that distribution was chosen every year (at every time step which equaled a year) for 100 years. In Option 2, the input distributions were scaled to represent the expected mean value of the input variable over the 100 years of simulation, and one value from that distribution was chosen for the entire elapsed time. Results show agreement across the three analysis tools. As expected, the linear models have identical results for the two different scaling Options, and the non-linear models diverge in the results for different time scale implementation. The implications of this work point to the need for careful consideration of input distribution temporal scaling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Operational Energy Life Cycle Data Development for the National Institute of Standards And Technology (NIST) Building Industry Reporting and Design for Sustainability (BIRDS) Neutral Environmental Software Tool (NEST)

For this analysis, regionalized life cycle assessment (LCA) results for environmental impacts (using the Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts [TRACI] 2.1) and cumulative energy demand (using the Federal Life Cycle Analysis Commons Elementary Flow List [FEDEFL] Inventory Methods v1.0.0) were evaluated for the production and utilization of electricity, natural gas, fuel oil, and propane as commodities within residential and commercial buildings. These results can used as a framework for future research into net zero, high-performance buildings, such as done here for the Building Industry Reporting and Design for Sustainability (BIRDS) database by the National Institute of Standards and Technology (NIST) Engineering Laboratory. The geographical results were assigned to each United States (U.S.) Zone Improvement Plan (ZIP) code based on the ZIP code location and corresponding Balancing Authority Area, natural gas basin, and Petroleum Administration for Defense Districts (PADDs). Additionally, previously developed models were utilized to develop future life cycle profiles. Projections were based on data available from the U.S. Energy Information Administration Annual Energy Outlook 2022 through 2050 (AEO 2022). Electricity LCA models were updated based on AEO 2022 projected annual generation mixes, while the natural gas baseline model was updated based on projected shares of natural gas types (conventional, shale, tight, and coalbed methane). Projections of crude oil production rates and export rates were applied to the petroleum baseline model in five-year increments to investigate their effects on the life cycle profile of fuel oil and propane. While only 100-year Global Warming Potential (GWP-100) with climate carbon feedback (CC-FB) and Cumulative Energy Demand are shown in Section 4: Results, the complete results, including Acidification Potential, Eutrophication Potential, Freshwater Ecotoxicity Potential, GWP-100 without inclusion of CC-FB, Human Health Impacts Potentials (Cancer, Non-Cancer), Ozone Depletion Potential, Particulate Matter Formation Potential, and Photochemical Smog Formation Potential, are tabulated for each ZIP code in the Excel worksheets that accompany this analysis. For the Excel spreadsheet tools associated with this report, please go to https://www.netl.doe.gov/energy-analysis/details?id=f8890fac-be55-44ac-aaa9-e2888bfabe93

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2002

Embedded Real-Time Linux for Instrument Control and Data Logging

When I moved to the west. coast to take a job at NASA's Ames Research Center in Mountain View, CA, I was impressed with the variety of equipment and software which scientists at the center use to conduct their research. was happy to find that I was just as likely to see a machine running Lenox as one running Windows in the offices and laboratories of NASA Ames (although many people seem to use Moos around here). I was especially happy to find that the particular group with whom I was going to work, the Atmospheric Physics Branch at Ames, relied almost entirely on Lenox machines for their day-to-day work. So it was no surprise that when it was time to construct a new control system for one of their most important pieces of hardware, a switch from an unpredictable DOS-based platform to an Embedded Linux-based one was a decision easily made. The system I am working on is called the Solar Spectral Flux Radiometer (SSFR), a PC-104 based system custom-built by Dr. Warren Gore at Ames. Dr. Gore, Dr. Peter Pilewskie, Dr. Maura Robberies and Larry Pezzolo use the SSFR in their research. The team working on the controller project consists of Dr. Gore, John Pommier, and myself. The SSFR is used by the ,cities Atmospheric Radiation Group to measure solar spectral irradiance at moderate resolution to determine the radiative effect of clouds, aerosols, and gases on climate, and also to infer the physical properties of aerosols and clouds. Two identical SSFR's have been built and successfully deployed in three field missions: 1) the Department of Energy Atmospheric Radiation Measurement (ARM) Enhanced Shortwave Experiment (ARESE) II in February/March, 2000; 2) the Puerto Rico Dust Experiment (PRIDE) in July, 2000; and 3) the South African Regional Science Initiative (SAFARI) in August/September, 2000. Additionally, the SSFR was used to acquire water vapor spectra using the Ames Diameter base-path multiple-reflection absorption cell in a laboratory experiment.

Clanton, Sam↗
DOE OSTI2012

Advanced, Integrated Control for Building Operations to Achieve 40% Energy Saving

We developed and demonstrated a software based integrated advanced building control platform called Smart Energy Box (SEB), which can coordinate building subsystem controls, integrate variety of energy optimization algorithms and provide proactive and collaborative energy management and control for building operations using weather and occupancy information. The integrated control system is a low cost solution and also features: Scalable component based architecture allows to build a solution for different building control system configurations with needed components; Open Architecture with a central data repository for data exchange among runtime components; Extendible to accommodate variety of communication protocols. Optimal building control for central loads, distributed loads and onsite energy resource; uses web server as a loosely coupled way to engage both building operators and building occupants in collaboration for energy conservation. Based on the open platform of SEB, we have investigated and evaluated a variety of operation and energy saving control strategies on Carnegie Mellon University Intelligent Work place which is equipped with alternative cooling/heating/ventilation/lighting methods, including radiant mullions, radiant cooling/heating ceiling panels, cool waves, dedicated ventilation unit, motorized window and blinds, and external louvers. Based on the validation results of these control strategies, they were integrated in SEB in a collaborative and dynamic way. This advanced control system was programmed and computer tested with a model of the Intelligent Workplace's northern section (IW n ). The advanced control program was then installed in the IW n control system; the performance was measured and compared with that of the state of the art control system to verify the overall energy savings great than 40%. In addition advanced human machine interfaces (HMI's) were developed to communicate both with building occupants and the building operator. Lifecycle cost analyses of the advanced building control were performed, and a Building Control System Guide was prepared and published to inform owners, architects, and engineers dealing with new construction or renovation of buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2020

NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%.

Chirayath, Ved↗