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NASA NTRS1991

Constraint-based scheduling

The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

Zweben, Monte↗

NASA NTRS1991

Constraint-based scheduling

The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocations for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its applications to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

Zweben, Monte↗

NASA NTRS1993

Constraint-based scheduling

Zweben, Monte↗

DOE OSTI2018

Homogenizing OSG and XSEDE: Providing Access to XSEDE Allocations through OSG Infrastructure

We present a system that allows individual researchers and virtual organizations (VOs) to access allocations on Stampede2 and Bridges through the Open Science Grid (OSG), a national grid infrastructure for running high throughput computing (HTC) tasks. Using this system, VOs and researchers are able to run larger workflows than can be done with OSG resources alone. This system allows a VO or user to run on XSEDE resources (with their allocation) using the same framework used with OSG resources. The system consists of two parts: the compute element (CE) that routes workloads to the appropriate user accounts and allocation on XSEDE resources, and simulated access to the CernVM Filesystem (CVMFS) servers used by OSG and VOs to distribute software and data. This allows jobs submitted through this system to work on a homogeneous environment regardless of whether they run on XSEDE HPC resources (like Stampede2 and Bridges) or OSG.

Thapa, Thapa,Suchandra↗

DOE OSTI2017

Decaf: Decoupled Dataflows for In Situ High-Performance Workflows

Decaf is a dataflow system for the parallel communication of coupled tasks in an HPC workflow. The dataflow can perform arbitrary data transformations ranging from simply forwarding data to complex data redistribution. Decaf does this by allowing the user to allocate resources and execute custom code in the dataflow. All communication through the dataflow is efficient parallel message passing over MPI. The runtime for calling tasks is entirely message-driven; Decaf executes a task when all messages for the task have been received. Such a messagedriven runtime allows cyclic task dependencies in the workflow graph, for example, to enact computational steering based on the result of downstream tasks. Decaf includes a simple Python API for describing the workflow graph. This allows Decaf to stand alone as a complete workflow system, but Decaf can also be used as the dataflow layer by one or more other workflow systems to form a heterogeneous task-based computing environment. In one experiment, we couple a molecular dynamics code with a visualization tool using the FlowVR and Damaris workflow systems and Decaf for the dataflow. In another experiment, we test the coupling of a cosmology code with Voronoi tessellation and density estimation codes using MPI for the simulation, the DIY programming model for the two analysis codes, and Decaf for the dataflow. Such workflows consisting of heterogeneous software infrastructures exist because components are developed separately with different programming models and runtimes, and this is the first time that such heterogeneous coupling of diverse components was demonstrated in situ on HPC systems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1980

Optimal control model predictions of system performance and attention allocation and their experimental validation in a display design study

The influence of different types of predictor displays in a longitudinal vertical takeoff and landing (VTOL) hover task is analyzed in a theoretical study. Several cases with differing amounts of predictive and rate information are compared. The optimal control model of the human operator is used to estimate human and system performance in terms of root-mean-square (rms) values and to compute optimized attention allocation. The only part of the model which is varied to predict these data is the observation matrix. Typical cases are selected for a subsequent experimental validation. The rms values as well as eye-movement data are recorded. The results agree favorably with those of the theoretical study in terms of relative differences. Better matching is achieved by revised model input data.

Johannsen, G.↗

DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

National Cost-effectiveness of ANSI/ASHRAE/IES Standard 90.1-2016

The purpose of this analysis is to examine the cost-effectiveness of the 2016 edition of ANSI/ASHRAE/IES Standard 90.1 (ASHRAE 2016). Standard 90.1 is developed by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Standard Standing Project Committee (SSPC) 90.1. It is the model energy standard for commercial and multi-family residential buildings over three floors (42 USC 6833). PNNL analyzed the cost-effectiveness of changes in Standard 90.1 from 90.1-2013 to 90.1-2016, as applied in commercial buildings across the United States. During the development of new editions of Standard 90.1, the cost-effectiveness of individual changes (addenda) is often calculated to support the deliberations of ASHRAE Standard Standing Project Committee (SSPC) 90.1. The ASHRAE process, however, does not include analysis of the cost-effectiveness of the entire package of addenda from one edition of the standard to the next, which is of particular interest to adopting State and local governments. Providing States with an analysis of cost-effectiveness may encourage more rapid adoption of newer editions of energy codes based on Standard 90.1. This information may also inform the development of future editions of Standard 90.1. To establish the cost-effectiveness of Standard 90.1-2016, three main tasks were addressed: (1) Identification of building elements impacted by the updated standard; (2) Allocation of associated installation, maintenance, and replacement costs; and (3) Cost-effectiveness analysis of required changes In addition to installation, maintenance, and replacement costs, energy cost differences were needed to determine cost-effectiveness. The energy costs for each edition of Standard 90.1 were determined previously under the development of Standard 90.1-2016, as described below. The current analysis builds on the previous PNNL analysis (as outlined in Section 5.2) of the energy use and energy cost saving impacts of Standard 90.1-2016 compared to previous editions. The overall energy savings analysis of Standard 90.1 utilized a suite of 16 prototype EnergyPlus building models in 15 climate locations representing all eight U.S. climate zones. Detailed methodology and overall energy saving results from Standard 90.1-2016 are documented in the DOE technical report titled Energy Savings Analysis: ANSI/ASHRAE/IES Standard 90.1-2016 (DOE. 2018b).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

SUSTAINING NUCLEAR SECURITY REGIMES THROUGH CONTINUOUS LEARNING EXPERIENCES

As Member States plan, implement, and ultimately sustain their nuclear security regimes, human resource development supporting these regimes are paramount. Human resource development broadly includes programs addressing education, training, and knowledge management. The International Atomic Energy Agency’s Implementing Guide, Sustaining a Nuclear Security Regime, highlights the importance of national-level support for assigning resources that help ensure States are able to develop and retain sufficient human resources in the short, medium, and long term. Determining the resources needed to support education, training, and knowledge management is not an easy task, and knowledge management is a practice often overlooked when States plan for and allocate human resources for a nuclear security regime. The paper highlights the importance of implementing knowledge management practices as part of sustaining a nuclear security regime. Given the availability of secure web-based knowledge management tools, a nuclear security human resource development program should not depend solely on direct human interactions. To that end, the paper will offer an approach for planning and implementing a knowledge management system by using the U.S. Department of Energy/National Nuclear Security Administration’s Nuclear Smuggling Detection and Deterrence Knowledge Management Website as a case study. This example offers useful lessons learned for States considering or actively developing their own nuclear security knowledge management efforts and human resource development.

Tremonte, Matthew M.↗

DOE OSTI2024

Tools And Methods to Analyze Plant Outage Schedule and Assist Schedulers in Improving Outage Resilience

Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining

Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace existing Deep Neural Networks (DNNs). Despite the remarkable triumphs, the prolonged turnaround time of Transformer models is a widely recognized roadblock. The variety of sequence lengths imposes additional computing overhead where inputs need to be zero-padded to the maximum sentence length in the batch to accommodate the parallel computing platforms. This paper targets the field-programmable gate array (FPGA) and proposes a coherent sequence length adaptive algorithm–hardware co-design for Transformer acceleration. Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. The proposed sparse attention operator brings the complexity of attention-based models down to linear complexity and alleviates the off-chip memory traffic. The proposed length-aware resource hardware scheduling algorithm dynamically allocates the hardware resources to fill up the pipeline slots and eliminates bubbles for NLP tasks. Experiments show that our design has very small accuracy loss and has 80.2 × and 2.6 × speedup compared to CPU and GPU implementation, and 4 × higher energy efficiency than state-of-the-art GPU accelerator optimized via CUBLAS GEMM.

Peng, Hongwu↗

NASA NTRS2011

Factors Influencing the Decisions and Actions of Pilots and Air Traffic Controllers in Three Plausible NextGen Environments

In the current air traffic management (ATM) system, pilots and air traffic controllers have well-established roles and responsibilities: pilots fly aircraft and are concerned with energy management, fuel efficiency, and passenger comfort; controllers separate aircraft and are concerned with safety and management of traffic flows. Despite having different goals and obligations, both groups must be able to effectively communicate and interact with each other for the ATM system to work. This interaction will become even more challenging as traffic volume increases dramatically in the near future. To accommodate this increase, by 2025 the national air transportation system in the U.S. will go through a transformation that will modernize the ATM system and make it safer, more effective, and more efficient. This new system, NextGen, will change how pilots and controllers perform their tasks by incorporating advanced technologies and employing new procedures. It will also distribute responsibility between pilots, controllers and automation over such tasks as maintaining aircraft separation. The present chapter describes three plausible concepts of operations that allocate different ATM responsibilities to these groups. We describe how each concept changes the role of each operator and the types of decisions and actions performed by them.

Vu, Kim-Phuong L.↗

DOE OSTI2023

A Novel Multi-Agent Deep Reinforcement Learning-enabled Distributed Power Allocation Scheme for mmWave Cellular Networks

We consider the power allocation problem over shared spectrum for millimeter-Wave (mmWave) cellular downlink. Existing approaches usually find sub-optimal solutions by solving a non-convex optimization which leads to scalability issues due to centralized control. Therefore, distributed and adaptive approaches are desirable. Recently, model-free Deep Reinforcement Learning (DRL) has achieved success in such wireless resource management tasks. By modeling the radio environment as a Markov Decision Process (MDP) with the base stations (BSs) being the agents, power allocation can be automated at the agent level with comparable throughput performance to conventional centralized schemes. The multi-agent setting presents new challenges as the radio environment is impacted by the joint actions of the agents and is no longer stationary from any individual agent’s perspective. Existing literature bypasses this non-stationarity violation by ignoring it which may cause performance degradation. To tackle this issue, we propose a distributed continuous power allocation scheme based on a modified version of multi-agent Deep Deterministic Policy Gradient (MADDPG) that is tailored for the distributed multiple-agent setting. The proposed scheme employs a centralized-training distributed-execution framework where Q-functions are trained over subsets of BSs while each BS determines its transmit power based only on its own local observation. It admits constant per-BS communication and computation complexity and is thus scalable to large networks. Numerical evaluation shows that the proposed scheme adapts well to a wide range of interference conditions and can achieve comparable or better performance than several state-of-the-art non-learning approaches.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS1987

Simulation Of Research And Development Projects

Measures of preference for alternative project plans calculated. Simulation of Research and Development Projects (SIMRAND) program aids in optimal allocation of research and development resources needed to achieve project goals. Models system subsets or project tasks as various network paths to final goal. Each path described in terms of such task variables as cost per hour, cost per unit, and availability of resources. Uncertainty incorporated by treating task variables as probabilistic random variables. Written in Microsoft FORTRAN 77.

Miles, Ralph F.↗

NASA NTRS1991

An enhanced Ada run-time system for real-time embedded processors

An enhanced Ada run-time system has been developed to support real-time embedded processor applications. The primary focus of this development effort has been on the tasking system and the memory management facilities of the run-time system. The tasking system has been extended to support efficient and precise periodic task execution as required for control applications. Event-driven task execution providing a means of task-asynchronous control and communication among Ada tasks is supported in this system. Inter-task control is even provided among tasks distributed on separate physical processors. The memory management system has been enhanced to provide object allocation and protected access support for memory shared between disjoint processors, each of which is executing a distinct Ada program.

Sims, J. T.↗

NASA NTRS2004

The Training Process of the Organization Development and Training Office

The Organization Development and Training Office provides training and development opportunities to employees at NASA Glenn Research Center, as a division of the Office of Human Resources and Workforce Planning. Center-wide required trainings, new employee trainings, workshops and career development programs are organized by the OD&TO staff. They also arrange all academic, non-academic, headquarters, fellowship and learning center sponsored courses. They also service organizations wishing to work more effectively by facilitating teambuilding exercises. Equal Opportunity programs and upward mobility programs such as the STEP and GO programs for administrative staff. In working with my mentor I am very involved with Cuyahoga Community College classes, mandatory supervisory training and administrative staff workshops. My largest tasks are in the secretarial training category. The Supporting Organizations And Relationships workshop for administrative personnel, commonly known as SOAR, began last year and continued this summer with follow-up workshops. Months before a workshop or class is brought to Glenn, a need has to be realized. In this case, administrative staff did not feel they had an opportunity to receive relevant training and develop skills through teambuilding, networking and communication. A Statement of work is then created as several companies are contacted about providing the training. After the company best suited to meet the target group s needs is selected, the course is announced with an outline of all pertinent information. A reservation for a facility is made and applications or nominations, depending on the announcement s guidelines, are received from interested employees. Confirmations are sent to participants and final preparations are made but there are still several concluding steps. A training office staff member also assists the facilitator with setting up the facility and introducing the class. After the class, participants evaluations are read and summarized to determine the effectiveness of the class and instructor. In addition to the SOAR workshops, I have several projects and daily tasks to complete. Coding training applications, which require me to be familiar with Glenn s budgetary allocations and policies on training, is an ongoing process. It also requires verifying information reported by an employee via her C-478 form, more commonly known as the training application. I am also the point of contact for the Cuyahoga Community College Advising Sessions held here at NASA Glenn which involves coordinating counselors visits with employees schedules. Two databases had to be created. The first database holds information on administrative staff, and the other tracks supervisors training histories. Through these assignments I gained experience in Microsoft Access 2002 and spreadsheet creation, communicating with co-workers, and successfully facilitating a training to serve specific purposes. With trainings and evaluations to assessment them, the Organization Development and Training Office can assure a quality product and continued customer satisfaction.

Johnson, Melissa S.↗

DOE OSTI2016

Improving User Notification on Frequently Changing HPC Environments

Today s HPC centers user environments can be very complex. Centers often contain multiple large complicated computational systems each with their own user environment. Changes to a system s environment can be very impactful; however, a center s user environment is, in one-way or another, frequently changing. Because of this, it is vital for centers to notify users of change. For users, untracked changes can be costly, resulting in unnecessary debug time as well as wasting valuable compute allocations and research time. Communicating frequent change to diverse user communities is a common and ongoing task for HPC centers. This paper will cover the OLCF s current processes and methods used to communicate change to users of the center s large Cray systems and supporting resources. The paper will share lessons learned and goals as well as practices, tools, and methods used to continually improve and reach members of the OLCF user community.

communication↗

NASA NTRS1983

Railroad safety program, task 2

Aspects of railroad safety and the preparation of a National Inspection Plan (NIP) for rail safety improvement are examined. Methodology for the allocation of inspection resources, preparation of a NIP instruction manual, and recommendations for future NIP, are described. A statistical analysis of regional rail accidents is presented with causes and suggested preventive measures included.

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