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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rooftop unit comparison calculator: a framework for comparing performance of rooftop units with building energy simulation

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. In conclusion, it also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2008

Generic Spacecraft Model for Real-Time Simulation

Generic Spacecraft is the name of an evolving library of software that provides for simulation of a generic spacecraft that can orbit the Earth and land on the Moon (and, eventually, on Mars). This library is incorporated into the Langley Standard Realtime Simulation in C++ (LaSRS++) software framework. The generic-spacecraft simulation serves as a test bed for modeling spacecraft dynamics, propulsion, control systems, guidance, and displays. The Generic Spacecraft library supplements the LaSRS++ framework with an interface that facilitates the connection of new models into the LaSRS++ simulation by eliminating what would otherwise be the necessity of writing additional C++ classes to record data from the models and code to display values on graphical user interfaces (GUIs): The library includes routines for integrating new models into the LaSRS++ framework, identifying model inputs and outputs with full descriptions and units identified, recording data, and automatically generating graphical user interfaces (GUIs). The library is designed to be used in a manner similar to that of LaSRS++ software components for simulating vehicles other than the generic spacecraft. The user specifies (1) a spacecraft and individual models to be constructed and (2) connections between individual model inputs and outputs.

Kenney, Patrick S.↗
DOE OSTI2020

Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency

Neuromorphic computing offers one path forward for AI at the edge. However, accessing and effectively utilizing a neuromorphic hardware platform is non-trivial. In this work, we present a complete pipeline for neuromorphic computing at the edge, including a small, inexpensive, low-power, FPGA-based neuromorphic hardware platform, a training algorithm for designing spiking neural networks for neuromorphic hardware, and a software framework for connecting those components. We demonstrate this pipeline on a real-world application, engine control for a spark-ignition internal combustion engine. We illustrate how we connect engine simulations with neuromorphic hardware simulations and training software to produce hardware-compatible spiking neural networks that perform engine control to improve fuel efficiency. We present initial results on the performance of these spiking neural networks and illustrate that they outperform open-loop engine control. We also give size, weight, and power estimates for a deployed solution of this type.

Schuman, Catherine↗
DOE OSTI2020

An Automatic Learning Framework for Smart Residential Communities

Predictability has been foundational to matching supply and demand in the day-to-day operation of the electric power system. Demand predictability is eroding because of the increased use of renewable energy resources and more sophisticated loads, such as electric vehicles and smart appliances. In this paper, an automatic software framework is described which can be used for load forecasting in smart communities. A time-varying clustering-based Markov chain approach is used to predict the energy consumption of residential buildings in a smart community. The training data is based on 1-minute meter data of occupied homes over one month. The data points are first clustered based on the energy consumption and the time of the day. Then, the original data is converted using the Centroids of the clusters. A time-varying Markov chain is subsequently trained to model the energy consumption behavior of residents for each home using the transformed data. The trained model is shown to successfully predict load in 5-minute intervals over a 24 hours period.

Zandi, Helia↗
NASA NTRS2012

Next Generation Simulation Framework for Robotic and Human Space Missions

The Dartslab team at NASA's Jet Propulsion Laboratory (JPL) has a long history of developing physics-based simulations based on the Darts/Dshell simulation framework that have been used to simulate many planetary robotic missions, such as the Cassini spacecraft and the rovers that are currently driving on Mars. Recent collaboration efforts between the Dartslab team at JPL and the Mission Operations Directorate (MOD) at NASA Johnson Space Center (JSC) have led to significant enhancements to the Dartslab DSENDS (Dynamics Simulator for Entry, Descent and Surface landing) software framework. The new version of DSENDS is now being used for new planetary mission simulations at JPL. JSC is using DSENDS as the foundation for a suite of software known as COMPASS (Core Operations, Mission Planning, and Analysis Spacecraft Simulation) that is the basis for their new human space mission simulations and analysis. In this paper, we will describe the collaborative process with the JPL Dartslab and the JSC MOD team that resulted in the redesign and enhancement of the DSENDS software. We will outline the improvements in DSENDS that simplify creation of new high-fidelity robotic/spacecraft simulations. We will illustrate how DSENDS simulations are assembled and show results from several mission simulations.

Dartslab↗
DOE OSTI2024

ROSE: A reduced-order scattering emulator for optical models

A new generation of phenomenological optical potentials requires robust calibration and uncertainty quantification, motivating the use of Bayesian statistical methods. These Bayesian methods usually require calculating observables for thousands or even millions of parameter sets, making fast and accurate emulators highly desirable or even essential. Emulating scattering across different energies or with interactions such as optical potentials is challenging because of the nonaffine parameter dependence, meaning the parameters do not all factorize from individual operators. Here we introduce and demonstrate the reduced-order scattering emulator ( ROSE ) framework, a reduced basis emulator that can handle nonaffine problems. ROSE is fully extensible and works within the publicly available band framework software suite for calibration, model mixing, and experimental design. As a demonstration problem, we use ROSE to calibrate a realistic nucleon-target scattering model through the calculation of elastic cross sections. This problem shows the practical value of the ROSE framework for Bayesian uncertainty quantification with controlled trade-offs between emulator speed and accuracy as compared to high-fidelity solvers. Finally, planned extensions of ROSE are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]

The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.

Emami, Patrick↗
DOE OSTI2025

CalderaCast User Manual Version 1.0

CalderaCast is a user-friendly web-based tool for electrical-load forecast, providing stakeholders with a fully customizable decision-support framework that estimates the likely power draw from a possible future electric-vehicle (EV) charging station at a given location on a given day along an alternative fuel corridor (AFC). These EV charging profiles are accurately modeled in CalderaCast using the Caldera software framework developed by Idaho National Laboratories (INL), reflecting the realistic charging levels observed in actual charge events. This tool was developed as part of the National Electric Vehicle Infrastructure (NEVI) program, which is quickly generating substantial interest from would-be charging station operators (CSO), large and small electric utilities, and state transportation planners, some of whom had not seriously considered EV charging previously. All these entities—with or without background in EV infrastructure—must estimate the electricity load that a proposed charging station will generate. This load forecast is critically important for a utility to properly assess the capacity of their distribution network to support the proposed station or properly size grid upgrades for potential load growth due to future EV adoption, vehicle technology improvements, or station growth. This document describes each aspect of the CalderaCast tool and provides guidance to users who are interested in utilizing the tool for their work.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Multidisciplinary Environments: A History of Engineering Framework Development

This paper traces the history of engineering frameworks and their use by Multidisciplinary Design Optimization (MDO) practitioners. The approach is to reference papers that have been presented at one of the ten previous Multidisciplinary Analysis and Optimization (MA&O) conferences. By limiting the search to MA&O papers, the authors can (1) identify the key ideas that led to general purpose MDO frameworks and (2) uncover roadblocks that delayed the development of these ideas. The authors make no attempt to assign credit for revolutionary ideas or to assign blame for missed opportunities. Rather, the goal is to trace the various threads of computer architecture and software framework research and to observe how these threads contributed to the commercial framework products available today.

Padula, Sharon L.↗
NASA NTRS2014

Multi-Source Autonomous Response for Targeting and Monitoring of Volcanic Activity

The study of volcanoes is important for both purely scientific and human survival reasons. From a scientific standpoint, volcanic gas and ash emissions contribute significantly to the terrestrial atmosphere. Ash depositions and lava flows can also greatly affect local environments. From a human survival standpoint, many people live within the reach of active volcanoes, and therefore can be endangered by both atmospheric (ash, debris) toxicity and lava flow. There are many potential information sources that can be used to determine how to best monitor volcanic activity worldwide. These are of varying temporal frequency, spatial regard, method of access, and reliability. The problem is how to incorporate all of these inputs in a general framework to assign/task/reconfigure assets to monitor events in a timely fashion. In situ sensing can provide a valuable range of complementary information such as seismographic, discharge, acoustic, and other data. However, many volcanoes are not instrumented with in situ sensors, and those that have sensor networks are restricted to a relatively small numbers of point sensors. Consequently, ideal volcanic study synergistically combines space and in situ measurements. This work demonstrates an effort to integrate spaceborne sensing from MODIS (Terra and Aqua), ALI (EO-1), Worldview-2, and in situ sensing in an automated scheme to improve global volcano monitoring. Specifically, it is a "sensor web" concept in which a number of volcano monitoring systems are linked together to monitor volcanic activity more accurately, and this activity measurement automatically tasks space assets to acquire further satellite imagery of ongoing volcanic activity. A general framework was developed for evidence combination that accounts for multiple information sources in a scientist-directed fashion to weigh inputs and allocate observations based on the confidence of an events occurrence, rarity of the event at that location, and other scientists' inputs. The software framework uses multiple source languages and is a general framework for combining inputs and incrementally submitting observation requests/reconfigurations, accounting for prior requests. The autonomous aspect of operations is unique, especially in the context of the wide range of inputs that includes manually inputted electronic reports (such as the Air Force Weather Advisories), automated satellite-based detection methods (such as MODVOLC and GOESVOLC), and in situ sensor networks.

Davies, Ashley G.↗
DOE OSTI2024

Virtual Resource Management Framework (CRADA Final Report)

There is a need for advanced and widespread business automation within the nuclear industry to drive down operational costs while sustaining or improving safe operations. While there are many business process automation platforms commercially available, the difficulty is that business processes typically rely on a mixture of resource types to accomplish the desired activities and there is no universal software framework virtualizing diverse resource types for the purpose of automation. In this context, we are referring to any capability, physical or intangible, that can be used by an organization to achieve its objectives as a resource. To deploy business automation broadly and enable integrated operations for nuclear (ION), a framework is needed to represent all resource types and their associated disparate data within a plant and to enable seamless flow of resource information to the technologies used for process automation and resource optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models

In recent years, reinforcement learning (RL) methods have been greatly enhanced by leveraging deep learning approaches. RL methods applied to building control have shown potential in many applications due to their ability to complement or replace conventional methods such as model-based or rule-based controls. However, RL-based building control software is likely tailored either to one target building system or to a specific RL method so that significant additional effort would be required to customize the RL-based controller for use in other building systems or with other RL approaches. Also, RL-based building controls usually depend on building energy simulations to train controllers, so emulating building dynamics (i.e., thermal dynamics and control dynamics) and capturing sub-hourly dynamic profiles are crucial to further the development of effective RL-based building control methods. To address these challenges, we present an open source RL-based control software employing a high-fidelity hybrid EnergyPlus-Modelica building energy model which emulates building dynamics at 1-minute resolution. This software consists of decoupled components (environment, building emulator, control agent, and RL algorithm), which allows for quick prototyping and benchmarking of standard RL algorithms in different systems; for example, a single component can be replaced without revising all of the software. To demonstrate this software framework, we conducted a benchmark study using an EnergyPlus-Modelica building energy model for a Chicago office building with an RL-based controller to dynamically control the chilled water temperature setpoint and the air handling unit supply air temperature setpoint on selected floors.

Lee, Joon-Yong↗
DOE OSTI2025

Calculation Of Neutrino Flux

The CONFLUX software framework, Calculation Of Neutrino FLUX, is built with the goal to simplify and standardize the calculation. CONFLUX packages three methods to calculate neutrinos generated from reactor neutrinos or individual beta decays with common nuclear data and beta theories for direct cross-method comparison. The software prepacked the latest nuclear database, including ENDF.B/VIII, JEFF 3.3, and ENSDF, as well as methods to process the uncertainties. It also allows customized nuclear data and beta theories and user generated time dependent reactor models for convenient adjustment of fission products, theoretical corrections.

Zhang, Xianyi [Lawrence Livermore National Laborat↗
DOE OSTI2022

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

McDonough, Benjamin↗
NASA NTRS1973

Feasibility study of an Integrated Program for Aerospace-vehicle Design (IPAD) system. Volume 6: Implementation schedule, development costs, operational costs, benefit assessment, impact on company organization, spin-off assessment, phase 1, tasks 3 to 8

A baseline implementation plan, including alternative implementation approaches for critical software elements and variants to the plan, was developed. The basic philosophy was aimed at: (1) a progressive release of capability for three major computing systems, (2) an end product that was a working tool, (3) giving participation to industry, government agencies, and universities, and (4) emphasizing the development of critical elements of the IPAD framework software. The results of these tasks indicate an IPAD first release capability 45 months after go-ahead, a five year total implementation schedule, and a total developmental cost of 2027 man-months and 1074 computer hours. Several areas of operational cost increases were identified mainly due to the impact of additional equipment needed and additional computer overhead. The benefits of an IPAD system were related mainly to potential savings in engineering man-hours, reduction of design-cycle calendar time, and indirect upgrading of product quality and performance.

Garrocq, C. A.↗
NASA NTRS1998

An Analysis of Failure Handling in Chameleon, A Framework for Supporting Cost-Effective Fault Tolerant Services

The desire for low-cost reliable computing is increasing. Most current fault tolerant computing solutions are not very flexible, i.e., they cannot adapt to reliability requirements of newly emerging applications in business, commerce, and manufacturing. It is important that users have a flexible, reliable platform to support both critical and noncritical applications. Chameleon, under development at the Center for Reliable and High-Performance Computing at the University of Illinois, is a software framework. for supporting cost-effective adaptable networked fault tolerant service. This thesis details a simulation of fault injection, detection, and recovery in Chameleon. The simulation was written in C++ using the DEPEND simulation library. The results obtained from the simulation included the amount of overhead incurred by the fault detection and recovery mechanisms supported by Chameleon. In addition, information about fault scenarios from which Chameleon cannot recover was gained. The results of the simulation showed that both critical and noncritical applications can be executed in the Chameleon environment with a fairly small amount of overhead. No single point of failure from which Chameleon could not recover was found. Chameleon was also found to be capable of recovering from several multiple failure scenarios.

Haakensen, Erik Edward↗
NASA NTRS2009

Towards a Framework for Evaluating and Comparing Diagnosis Algorithms

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

Kurtoglu, Tolga↗
DOE OSTI2024

The ATLAS experiment software on ARM

With an increased dataset obtained during the Run 3 of the LHC at CERN and the even larger expected increase of the dataset by more than one order of magnitude for the HL-LHC, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM architecture is becoming a competitive and energy efficient alternative. Some surveys indicate its increased presence in HPCs and commercial clouds, and some WLCG sites have expressed their interest. Chip makers are also developing their next generation solutions on ARM architectures, sometimes combining ARM and GPU processors in the same chip. Consequently it is important that the ATLAS software embraces the change and is able to successfully exploit this architecture. We report on the successful porting to ARM of the Athena software framework, which is used by ATLAS for both online and offline computing operations. Furthermore we report on the successful validation of simulation workflows running on ARM resources. For this we have set up an ATLAS Grid site using ARM compatible middleware and containers on Amazon Web Services (AWS) ARM resources. The ARM version of Athena is fully integrated in the regular software build system and distributed in the same way as other software releases. In addition, the workflows have been integrated into the HEPscore benchmark suite which is the planned WLCG wide replacement of the HepSpec06 benchmark used for Grid site pledges. In the overall porting process we have used resources on AWS, Google Cloud Platform (GCP) and CERN. A performance comparison of different architectures and resources will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗