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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 OSTI2019

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four-level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft-linked investment and operational models, (3a) bidirectionally soft-linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co-optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Some remarks on quasi-Hermitian operators

A quasi-Hermitian operator is an operator that is similar to its adjoint in some sense, via a metric operator, i.e., a strictly positive self-adjoint operator. Whereas those metric operators are in general assumed to be bounded, we analyze the structure generated by unbounded metric operators in a Hilbert space. Following our previous work, we introduce several generalizations of the notion of similarity between operators. Then we explore systematically the various types of quasi-Hermitian operators, bounded or not. Finally, we discuss their application in the so-called pseudo-Hermitian quantum mechanics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Risk Analysis for Remote Operation of Microreactors

Microreactors are a subset of advanced nuclear reactors that can be factory fabricated, transportable, and self-regulating. They have the potential to be used in microgrids, rural and remote areas, or emergency response applications, replacing fossil fuel sources like diesel generators and enabling sustainable energy generation. In order to make microreactor operation cost-effective, it is likely that remote communications will be needed to reduce the number of personnel required to be on site. While remote operation of energy generation and other industrial control systems is common in other industries, it is not yet adopted in the nuclear community and has many perceived and actual risks. In this paper, the severity of the risks introduced by remote operations for microreactors are explored. The primary changes in the operations involve the addition of a remote communications network and a certification system for data and controls. These changes lend themselves to considerations of cyber risks, whether unintentional or adversarial, but the assessment considers not just cyber risks introduced, but also how physical and human factors-based risks will impact the remote operations system and change the overall risk profile. This initial assessment indicates that there are standard cyber and mitigation measures that can be put in place so the risk of doing remote operations does not dramatically increase compared to local operations. This evaluation is a critical step in the process of evaluating if remote operations of microreactors is a suitable solution to meet future sustainable grid needs

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2021

Practical Operational Readiness Gambits: Operations Training Simulations for the Curiosity Rover

The Mars Science Laboratory team had been puttingin effort to make a Training Venue to allow for parallel shadowtactical operations for trainees to actively work alongside theprime tactical operations personnel without affecting operations,since staffing constraints and shortened operations timelineswere straining the tactical process in supporting operationstrainees in the traditional way. The COVID- 19 Pandemic presentedfurther challenges in continuing on-console training forMSL operations trainees. The MSL operations team switched tofully remote operations, thus hampering the direct mentorshipa trainee would normally receive while on site at JPL. TheMars 2020 training team had developed a concept for roveroperations training simulations based on Johnson Space Center’sextensive simulations training program for astronauts andflight controllers. The MSL team borrowed this idea, and implementedthese training simulations which are called PracticalOperational Readiness Gambits (PORGs). The PORGs so farhave focused on the Science Planner and Rover Planner roles,which are two crucial roles in tactical operations that engagein key interactions throughout a shift. PORGs are based onactual sol scenarios that have occurred on Mars and follow thetactical operations process and timeline as closely as possible.However, unlike an operations shift, PORGs can slow down orpause to allow for more mentoring time. PORGs can focus onparticular skills to test the trainees on their understanding ofa concept. PORGs increase in complexity with each scenario toease the trainees into more typical tactical operations workloads.As more trainees join the MSL operations team, more roles arebeing incorporated into PORGs. There are plans to incorporatecertified operations personnel into PORGs to practice anomalyresponse situations. PORGs have become an essential part of theMSL operations training program and will continue even afterthe return to on-site operations at JPL.

Gajeway, Jocelyn↗
DOE OSTI2016

On the quantum Landau collision operator and electron collisions in dense plasmas

The quantum Landau collision operator, which extends the widely used Landau/Fokker-Planck collision operator to include quantum statistical effects, is discussed. The quantum extension can serve as a reference model for including electron collisions in non-equilibrium dense plasmas, in which the quantum nature of electrons cannot be neglected. In this paper, the properties of the Landau collision operator that have been useful in traditional plasma kinetic theory and plasma transport theory are extended to the quantum case. We outline basic properties in connection with the conservation laws, the H-theorem, and the global and local equilibrium distributions. We discuss the Fokker-Planck form of the operator in terms of three potentials that extend the usual two Rosenbluth potentials. We establish practical closed-form expressions for these potentials under local thermal equilibrium conditions in terms of Fermi-Dirac and Bose-Einstein integrals. We study the properties of linearized quantum Landau operator, and extend two popular approximations used in plasma physics to include collisions in kinetic simulations. We apply the quantum Landau operator to the classic test-particle problem to illustrate the physical effects embodied in the quantum extension. We present useful closed-form expressions for the electron-ion momentum and energy transfer rates. Throughout the paper, similarities and differences between the quantum and classical Landau collision operators are emphasized.

Daligault, Jérôme↗
DOE OSTI2019

Modeling Load Dynamics to Support Resiliency-based Operations in Low-Inertia Microgrids

Microgrids have repeatedly demonstrated the ability to provide uninterrupted service to critical end-use loads during normal outages, severe weather events, and natural disasters. While their ability to provide critical services is well documented, microgrids present a more dynamic operational environment than grid-connected distribution systems. The dynamics of a microgrid are commonly driven by the high inertia of rotating generators, which are common in many microgrids. In such high-inertia systems, the impact of end-use load dynamics are often not examined. However, with the increased penetration of inverter-based generation with little or no inertia, it is necessary to consider the impact that the dynamics of the end-use loads have on the operations of microgrids, particularly for a resiliency-based operation. These operations include, but are not limited to, switching operations, loss of generating units, and the starting of induction motors. This paper examines the importance of including dynamic models of the end-use load when evaluating the operations of low inertia microgrids, and shows that by properly representing their behavior, it is possible to cost effectively size equipment while supporting resilient operations of critical end-use loads.

Tuffner, Francis K.↗
NASA NTRS2000

Lessons Learned on Operating and Preparing Operations for a Technology Mission from the Perspective of the Earth Observing-1 Mission

The New Millennium Program's first Earth-observing mission (EO-1) is a technology validation mission. It is managed by the NASA Goddard Space Flight Center in Greenbelt, Maryland and is scheduled for launch in the summer of 2000. The purpose of this mission is to flight-validate revolutionary technologies that will contribute to the reduction of cost and increase of capabilities for future land imaging missions. In the EO-1 mission, there are five instrument, five spacecraft, and three supporting technologies to flight-validate during a year of operations. EO-1 operations and the accompanying ground system were intended to be simple in order to maintain low operational costs. For purposes of formulating operations, it was initially modeled as a small science mission. However, it quickly evolved into a more complex mission due to the difficulties in effectively integrating all of the validation plans of the individual technologies. As a consequence, more operational support was required to confidently complete the on-orbit validation of the new technologies. This paper will outline the issues and lessons learned applicable to future technology validation missions. Examples of some of these include the following: (1) operational complexity encountered in integrating all of the validation plans into a coherent operational plan, (2) initial desire to run single shift operations subsequently growing to 6 "around-the-clock" operations, (3) managing changes in the technologies that ultimately affected operations, (4) necessity for better team communications within the project to offset the effects of change on the Ground System Developers, Operations Engineers, Integration and Test Engineers, S/C Subsystem Engineers, and Scientists, and (5) the need for a more experienced Flight Operations Team to achieve the necessary operational flexibility. The discussion will conclude by providing several cost comparisons for developing operations from previous missions to EO-1 and discuss some details that might be done differently for future technology validation missions.

Mandl, Dan↗
NASA NTRS2011

Co-Operative Advances in Behavioral Health and Performance Research and Operations

In organizations that engage in both operations and applied research, with operational needs guiding research questions and research informing improved operations, the ideal goal is a synergy of ideas and information. In reality, this ideal synergy is often lacking. Real-time operational needs driving day-to-day decisions, lack of communication, lag time in getting research advances plugged into operations can cause both areas to suffer from this gap between operations and research. At Johnson Space Center, the Behavior Health and Performance group (BHP) strives to bridge this gap by following a Human Research Program framework: Expectations of future operational needs identify the knowledge gaps; the gaps in turn guide research leading to a product that is transitioned into operations. Thus, the direction those of us in research take is in direct response to current and future needs of operations. Likewise, those of us in operations actively seek knowledge that is supported by evidence-based research. We make an ongoing effort to communicate across the research and operations gap by working closely with each other and making a conscious effort to keep each other informed. The objective of the proposed panel discussion is to demonstrate through the following presentations the results of a successful collaboration between research and operations and to provide ASMA members with more practical knowledge and strategies for building these bridges to serve our field of practice well. The panel will consist of six presenters from BHP operations, internal BHP research, and external research instigated by BHP who together represent the entire BHP Research Transition to Operations Framework

VanderArk, Stephen T.↗
NASA NTRS2012

Desert Research and Technology Studies (DRATS) 2010 Science Operations: Operational Approaches and Lessons Learned for Managing Science during Human Planetary Surface Missions

Desert Research and Technology Studies (Desert RATS) is a multi-year series of hardware and operations tests carried out annually in the high desert of Arizona on the San Francisco Volcanic Field. These activities are designed to exercise planetary surface hardware and operations in conditions where long-distance, multi-day roving is achievable, and they allow NASA to evaluate different mission concepts and approaches in an environment less costly and more forgiving than space.The results from the RATS tests allows election of potential operational approaches to planetary surface exploration prior to making commitments to specific flight and mission hardware development. In previous RATS operations, the Science Support Room has operated largely in an advisory role, an approach that was driven by the need to provide a loose science mission framework that would underpin the engineering tests. However, the extensive nature of the traverse operations for 2010 expanded the role of the science operations and tested specific operational approaches. Science mission operations approaches from the Apollo and Mars-Phoenix missions were merged to become the baseline for this test. Six days of traverse operations were conducted during each week of the 2-week test, with three traverse days each week conducted with voice and data communications continuously available, and three traverse days conducted with only two 1-hour communications periods per day. Within this framework, the team evaluated integrated science operations management using real-time, tactical science operations to oversee daily crew activities, and strategic level evaluations of science data and daily traverse results during a post-traverse planning shift. During continuous communications, both tactical and strategic teams were employed. On days when communications were reduced to only two communications periods per day, only a strategic team was employed. The Science Operations Team found that, if communications are good and down-linking of science data is ensured, high quality science returns is possible regardless of communications. What is absent from reduced communications is the scientific interaction between the crew on the planet and the scientists on the ground. These scientific interactions were a critical part of the science process and significantly improved mission science return over reduced communications conditions. The test also showed that the quality of science return is not measurable by simple numerical quantities but is, in fact, based on strongly non-quantifiable factors, such as the interactions between the crew and the Science Operations Teams. Although the metric evaluation data suggested some trends, there was not sufficient granularity in the data or specificity in the metrics to allow those trends to be understood on numerical data alone.

Eppler, Dean↗
NASA NTRSDate not supplied

Astrobee Operations on the Iss: Gui’S Impact on the Operators’ Cognitive Load

The Astrobee free-flying robots have completed their fifth successful year of operations housed in the Japanese Experimental Module (JEM) on the International Space Station (ISS). In this paper, we introduce two Graphical User Interfaces (GUI) used to operate and monitor in real-time the Astrobee free-flying robots onboard the ISS and report on the impact these GUIs have in the operator’s cognitive load. A review of the state-of-the-art GUI design for remotely teleoperated scenarios with minimal time delay is presented and the study’s conclusion used to determine the elements and recommendations to create an interface that minimizes its impact on the overall performance of an operator during an activity at the ISS. The Ground Data System (GDS) is one of the two GUIs in the study: it contains several tabs, each of which displays a different set of controls for specific tasks e.g. Overview, Run Plan, Teleoperate, Guest Science; some also display video and a three-dimensional (3D) representation of the ISS and robot based on the Astrobee’s telemetry. Most tabs enable a single operator-robot connection, however some of its tabs are capable to monitor and control up to three Astrobees simultaneously. The GDS Helper is a text-based user interface created to facilitate commanding and monitoring of an Astrobee robot directly from an SSH session. In full interactive mode it displays a maximum of 5 sections: general commanding, feedback/ack, telemetry, guest science commanding, and data, all in one view. In batch mode, it enables complex command scripting while retaining some interactive capabilities. A comparative analysis between these GUIs is carried out at an analogous ISS environment at the NASA Ames Research Center’s Granite Lab and its results presented. While GDS is able to provide an operator with control and situational awareness via its video and 3D displays, its several tabs may introduce an overwhelming amount of information confusing and delaying the operator especially during time-sensitive maneuvers where the operator may need to switch back and forth between them. GDS helper in the other hand does not provide video or 3D displays thus not allowing an operator to attain situational awareness, however it provides the operator with a design displaying commonly used data in a single window, enabling the operator to understand the state of the robot at a glance and control it through a commands entered via keyboard instead of a combination of mouse clicks and keyboard input. The results of the experiments measure the cognitive load across several operators maneuvering Astrobee to accomplish tasks ranging from fully manual to supervised activities. A GUI combining a single window displaying data along video and a 3D display is expected to reduce the operator’s cognitive load.

Astrobee↗
DOE OSTI2022

MIONet: Learning Multiple-Input Operators via Tensor Product

As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all the existing neural operators are only designed to learn operators defined on a single Banach space; i.e., the input of the operator is a single function. Here, for the first time, we study the operator regression via neural networks for multiple-input operators defined on the product of Banach spaces. We first prove a universal approximation theorem of continuous multiple-input operators. We also provide a detailed theoretical analysis including the approximation error, which provides guidance for the design of the network architecture. Based on our theory and a low-rank approximation, we propose a novel neural operator, MIONet, to learn multiple-input operators. MIONet consists of several branch nets for encoding the input functions and a trunk net for encoding the domain of the output function. Here, we demonstrate that MIONet can learn solution operators involving systems governed by ordinary and partial differential equations. In our computational examples, we also show that we can endow MIONet with prior knowledge of the underlying system, such as linearity and periodicity, to further improve accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ROTOR: Research to Operations and Operations to Research

PNNL’s Research to Operations/Operations to Research (ROTOR) Program fosters a cybersecurity operations to researcher collaboration, identifying hard problems and challenges for cyber defenders and bringing researcher science disciplines to these challenges. ROTOR allows PNNL’s cyber defenders to better protect the laboratory but also fosters innovation and incubation of solutions for our sponsor missions across DOE, DOD, DHS and the intelligence community. Our sponsors and their missions are faced with many of the same cyber defense challenges that PNNL faces. By using our own laboratory security operations environment as an innovation generator and testing ground, allowing research to be tested and tried, our sponsors are directly benefitted. PNNL is in a unique position to combine our world class research organization with enterprise cybersecurity operations. ROTOR is taking advantage of this to bring PNNL cybersecurity research projects into an operational environment within PNNL’s Cyber Security Operations Center. With ROTOR, PNNL researchers gain the advantage of operational experience and expertise and have an avenue for showcasing research in an operational environment. This helps to advance PNNL research, prove operability of PNNL projects to its sponsors, and provide real-world insight to real-world problems for our cybersecurity research agenda. ROTOR is the conduit for PNNL cybersecurity research to find its way to operational use. PNNL researchers and engineers are informed by real-world operations and operations has access to current PNNL research and engineering capabilities. All of this leads to improved reputation for PNNL as a provider of national security solutions that are tried and tested. To date, ROTOR has collaborated with six projects, enabling each to conduct operational work within the CSOC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Filtering micro-operations for a micro-operation cache in a processor

A processor includes a micro-operation cache having a plurality of micro-operation cache entries for storing micro-operations decoded from instruction groups and a micro-operation filter having a plurality of micro-operation filter table entries for storing identifiers of instruction groups for which the micro-operations are predicted dead on fill if stored in the micro-operation cache. The micro-operation filter receives an identifier for an instruction group. The micro-operation filter then prevents a copy of the micro-operations from the first instruction group from being stored in the micro-operation cache when a micro-operation filter table entry includes an identifier that matches the first identifier.

Scrbak, Marko↗
NASA NTRS2024

Operational Integration Assessment (OIA) of Midterm UAM Operations: Class C Airspace Tabletop Exercise and Integration Checkpoint

The National Aeronautics and Space Administration (NASA), in collaboration with the Federal Aviation Administration (FAA), is conducting research into evolving today’s air traffic management system towards a more automated and operationally flexible airspace to accommodate Urban Air Mobility (UAM) operations at scale. UAM operations, enabled by electric Vertical Takeoff and Landing (eVTOL) aircraft, may change the role of aviation in the movement of people and goods and provide practical, cost-effective air transport in metropolitan areas. FAA UAM Concept of Operations v2.0 describes three evolutionary stages of UAM operations: Initial, Midterm, and Mature State operations. Midterm operations are comprised of many complex changes to the national airspace system (NAS). The Operational Integration Assessment (OIA) was created as a capability to address the need to study the progression and identify interdependencies of those changes that may occur during the midterm UAM operations timeframe. The OIA includes a series of tabletop exercises and integration checkpoints planned to explore various use cases from end-to-end, evaluated by NASA’s Air Traffic Management eXploration (ATM-X) project in partnership with the FAA’s William J. Hughes Technical Center (WJHTC) and industry partners. The use cases were exercised in an immersive, integrated live-virtual-constructive (LVC) airspace simulation environment, called the NASA/FAA Laboratory Integrated Test Environment (NFLITE), as part of an effort to learn how UAM operations can scale beyond the as-is NAS and through the transition to higher-tempo and highly automated operations of the future. This document describes the events of the tabletop exercise held from January 24-26, 2023, at the National Airspace Research & Technology Park (NARTP) in Egg Harbor Township, New Jersey, adjacent to the WJHTC and the subsequent integration checkpoint performed on March 28, 2023,at NASA Langley Research Center (LaRC) in Hampton, Virginia.

UAM↗
DOE OSTI2020

Design of a Non-PLL Grid-Forming Inverter for Smooth Microgrid Transition Operation

This paper develops a controller for a grid-forming (GFM) inverter that is capable of operating as either a GFM or grid-feeding source that can improve the operation of a microgrid during on-off grid transitions through use of a novel synchronization approach. Furthermore, this controller avoids use of a phase-locked loop (PLL) and the inverter is able to synchronize with the grid with self-generated voltage and frequency. This prevents the inverter from replicating any grid voltage disturbances in its output—a key disadvantage of many grid-connected inverters that use a PLL. To enable fast synchronization, active synchronization control is adopted both during inverter start-up and microgrid reconnection operation and a method of coordinating synchronization of the inverter with a microgrid controller and grid interconnection circuit breaker is presented. Simulation results for multiple microgrid transition operations and unplanned islanding events demonstrate that the developed non-PLL grid-connected GFM inverter controller and synchronization method are effective in synchronizing the inverter and microgrid to the grid, avoiding phase jump during microgrid transition operation, and improving microgrid islanding transients versus a traditional configuration.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2016

A fully non-linear multi-species Fokker–Planck–Landau collision operator for simulation of fusion plasma

Fusion edge plasmas can be far from thermal equilibrium and require the use of a non-linear collision operator for accurate numerical simulations. In this article, the non-linear single-species Fokker–Planck–Landau collision operator developed by Yoon and Chang (2014) [9] is generalized to include multiple particle species. The finite volume discretization used in this work naturally yields exact conservation of mass, momentum, and energy. The implementation of this new non-linear Fokker–Planck–Landau operator in the gyrokinetic particle-in-cell codes XGC1 and XGCa is described and results of a verification study are discussed. Finally, the numerical techniques that make our non-linear collision operator viable on high-performance computing systems are described, including specialized load balancing algorithms and nested OpenMP parallelization. The collision operator's good weak and strong scaling behavior are shown.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗