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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 OSTI2018

Zero Energy University Campuses: A 2018 Progress Update on Reaching Campus Energy Goals: Preprint

Many universities have aggressive energy-reduction and climate action plans. To reach these goals, leading universities have developed a suite of common energy-efficiency and renewable generation strategies. In this paper, we present a common set of approaches across the following areas: zero energy campus definitions and planning, best practices for including energy targets in new construction procurement contracts and design guidelines, implementing pilot new construction zero energy projects, aggressively implementing best-in-class laboratory energy-efficiency strategies, planning for and optimizing on-site and off-site renewable energy projects, and high-performance district energy systems. Best practices are compiled from various experiences, including programs funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy program. The summary of these best practices is to reduce campus energy use and then meet the remaining campus energy needs with renewable energy. The fivestep process to developing and implementing campus energy-reduction plans is described: 1) determine baseline energy consumption, 2) analyze technology options, 3) develop a plan and set priorities, 4) implement the plan, and 5) evaluate progress. The process follows a logical hierarchy of actions to evaluate options by energy sector and set specific targets. It encompasses all energy systems on campus, recognizing that campus-wide measures have increased potential for reducing energy use. Common strategies are explained along with examples and results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Zero Energy University Campuses: A 2018 Progress Update on Reaching Campus Energy Goals

Many universities have aggressive energy-reduction and climate action plans. To reach these goals, leading universities have developed a suite of common energy-efficiency and renewable generation strategies. In this paper, we present a common set of approaches across the following areas: zero energy campus definitions and planning, best practices for including energy targets in new construction procurement contracts and design guidelines, implementing pilot new construction zero energy projects, aggressively implementing best-in-class laboratory energy-efficiency strategies, planning for and optimizing on-site and off-site renewable energy projects, and high-performance district energy systems. Best practices are compiled from various experiences, including programs funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy program. The summary of these best practices is to reduce campus energy use and then meet the remaining campus energy needs with renewable energy. The fivestep process to developing and implementing campus energy-reduction plans is described: 1) determine baseline energy consumption, 2) analyze technology options, 3) develop a plan and set priorities, 4) implement the plan, and 5) evaluate progress. The process follows a logical hierarchy of actions to evaluate options by energy sector and set specific targets. It encompasses all energy systems on campus, recognizing that campus-wide measures have increased potential for reducing energy use. Common strategies are explained along with examples and results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2006

Systems Engineering and Integration for Technology Programs

The Architecture, Habitability & Integration group (AH&I) is a system engineering and integration test team within the NASA Crew and Thermal Systems Division (CTSD) at Johnson Space Center. AH&I identifies and resolves system-level integration issues within the research and technology development community. The timely resolution of these integration issues is fundamental to the development of human system requirements and exploration capability. The integration of the many individual components necessary to construct an artificial environment is difficult. The necessary interactions between individual components and systems must be approached in a piece-wise fashion to achieve repeatable results. A formal systems engineering (SE) approach to define, develop, and integrate quality systems within the life support community has been developed. This approach will allow a Research & Technology Program to systematically approach the development, management, and quality of technology deliverables to the various exploration missions. A tiered system engineering structure has been proposed to implement best systems engineering practices across all development levels from basic research to working assemblies. These practices will be implemented through a management plan across all applicable programs, projects, elements and teams. While many of the engineering practices are common to other industries, the implementation is specific to technology development. An accounting of the systems engineering management philosophy will be discussed and the associated programmatic processes will be presented.

Kennedy, Kruss J.↗
NASA NTRS2020

Planning and Optimization for Multi-robot Planetary Cave Exploration under Intermittent Connectivity Constraints

Exploring subsurface structures with autonomous robots is of growing interest in the context of planetary caves studies. Communication between robots in these environments is severely degraded which complicates coordination and information distribution. In this paper we focus on planning for mobility and communication in a cave exploration scenario where the situational awareness of a static base station is critical. We propose a notion of information-consistency where a plan itself is part of the information to be shared between robots, and propose a method for generating information-consistent plans. We discuss in detail how the resulting plan can be robustly implemented with minimal communication through local mission executives that run on individual robots. We describe preliminary results on the performance of the planning algorithm and integration of the local mission executives in a high-fidelity simulation environment.

Murray, Richard M.↗
NASA NTRS1985

Report of the LSPI/NASA Workshop on Lunar Base Methodology Development

Groundwork was laid for computer models which will assist in the design of a manned lunar base. The models, herein described, will provide the following functions for the successful conclusion of that task: strategic planning; sensitivity analyses; impact analyses; and documentation. Topics addressed include: upper level model description; interrelationship matrix; user community; model features; model descriptions; system implementation; model management; and plans for future action.

Nozette, Stewart↗
NASA NTRSDate not supplied

James Webb Space Telescope Navigation Optimization Challenges

The James Webb Space Telescope (JWST) is a NASA flagship mission launched on December 25, 2021. The early orbit phase was highlighted by three midcourse correction burns that were performed to maneuver the vehicle into a libration orbit at the second Sun-Earth-Moon (SEM) libration point (L2). During the coast to L2, the vehicle’s primary observatory and sunshield were deployed. Once the tennis-court-sized sunshield was unfurled and tensioned into place, the large area exposed to solar radiation pressure (SRP) dictated that the SRP force model would need to account for the vehicle’s geometry, reflective properties, and orientation relative to the Sun. Following the insertion of JWST into its L2 libration orbit on January 24, 2022, the commissioning phase for the vehicle’s observatory commenced along with the first cycle of routine station-keeping maneuvers. The NASA Goddard Space Flight Center’s Flight Dynamics Facility (FDF) provides navigation services for the JWST mission. The FDF serves as the prime or backup navigation operations center for more than 30 active missions spanning a wide array of flight regimes performing orbit determination, maneuver planning, trajectory optimization, and tracking data evaluation. Definitive orbit determination for JWST is performed by the FDF using an Extended Kalman Filter (EKF) to estimate the vehicle’s trajectory and reflective properties using Deep Space Network (DSN) TRK-2-34 tracking data and spacecraft attitude telemetry. For the purposes of orbit prediction and station-keeping maneuver targeting, the sensitivity of the SRP force to the vehicle’s orientation requires that predictive attitude information must be incorporated to generate accurate orbit predictions and maneuver plans. Predictive attitude plans are provided to the FDF by the JWST Spacecraft Operations Center (SOC). Short-term (28 day) orbit predictions are propagated using a Short Range Attitude Plan (SRAP) to model the vehicle’s future attitude states for up to a week. SRAP attitude data is highly reliable, as it reflects finalized planned attitude states that the vehicle will be commanded to attain during the ensuing week. A Long Range Attitude Plan (LRAP) can be implemented to model predicted attitude states beyond one week, but these predictions are tentative and subject to revision due to changes in science operations plans. Instead, a conservative approach of applying a Sun-Pointing Neutral (SPN) attitude configuration is utilized for long-term (2 year) orbit predictions where reliable planned attitude data is not available. SPN attitude mode aligns the net SRP force along the JWST-to-Sun vector where it becomes independent of the vehicle’s angle of rotation about this vector, defined as the Sun yaw. Given the dynamic instability of the libration orbit, station-keeping thrust must be applied in either the sunward or anti-sunward direction to balance the resulting orbit. The attitude constraints of the vehicle also impose limits on the available pointing directions for station-keeping thrusters. The thrusters cannot be aligned with the optimal pointing direction for sunward maneuvers, rendering sunward maneuvers to be significantly less fuel efficient than anti-sunward maneuvers. Consequently, station-keeping maneuvers must be targeted to balance the orbit while ensuring that the next maneuver will also be performed in the anti-sunward direction to optimize propellant usage. When targeting a station-keeping maneuver, the attitude and SRP modeling configuration which is applied to the predicted post-maneuver trajectory will dictate the direction of the subsequent station-keeping maneuver, assuming nominal propulsion system performance. If the predicted post-maneuver attitude states result in an SRP model which under-predicts the cumulative SRP impact on the post-maneuver orbit, a targeted anti-sunward maneuver will be larger than necessary and the next maneuver will need to be executed sunward in order to compensate. In contrast, an anti-sunward maneuver which is targeted using an SRP model which over-predicts the cumulative SRP impact will achieve station-keeping while ensuring that the next maneuver will likewise be performed anti-sunward. For this reason, station-keeping maneuvers are targeted while applying the SPN attitude mode, as this mode entails the largest possible SRP area cross-section and therefore a larger modeled cumulative SRP impact. This paper documents the NASA Goddard Space Flight Center’s FDF support for JWST on-orbit operations and the analysis projects undertaken utilizing the experiences and data accumulated throughout the first full year of routine science operations. The results of these analysis efforts have been used to implement improvements to orbit prediction accuracy and maneuver efficiency which have the potential to prolong the lifespan of JWST to continue to conduct ground-breaking infrared astronomy for decades to come.

Flight Dynamics↗
DOE OSTI2018

Hanford Site Ecological Monitoring Report for Calendar Year 2017

The U.S. Department of Energy, Richland Operations Office (DOE-RL) conducts ecological monitoring on the Hanford Site to collect and track data needed to ensure compliance with an array of environmental laws, regulations, and policies governing the U.S. Department of Energy’s (DOE) activities. Ecological monitoring data provide baseline information about the plants, animals, and habitats under DOE-RL stewardship at the Hanford Site required for decision making under the National Environmental Policy Act and Comprehensive Environmental Response, Compensation, and Liability Act. DOE/EIS-0222-FU, Final Hanford Comprehensive Land Use Plan Environmental Impact Statement, (CLUP) evaluates potential environmental impacts associated with implementing a comprehensive land use plan for the Hanford Site over the next 50 years. The vision for the DOE-RL-managed portion of the Hanford Site focuses not only on the cleanup of nuclear facilities and waste sites but on the protection of groundwater, the Columbia River, and the restoration of the land for access and use. To reach these goals DOE-RL is working closely with partners, such as the U. S. Fish and Wildlife Service (FWS) and National Park Service, to enable use of the Hanford land consistent with the CLUP. As DOE-RL moves toward accomplishing this vision, monitoring the ecological resources present to determine whether there is a need for conservation and/or protection of any resources will be critical for making informed decisions for responsible site stewardship.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

All Kitted Out

The Hydropower Knowledge Sharing and Succession Planning Toolkit, created by the National Renewable Energy Laboratory (NREL) in collaboration with the U.S. Department of Energy’s Water Power Technologies Office (WPTO), can help your organization effectively plan for workforce changes and share knowledge across employees. By proactively implementing knowledge sharing and succession planning strategies, hydropower organizations can safeguard institutional knowledge, enhance workforce resilience, and ensure long-term operational stability in an evolving industry landscape.

13 HYDRO ENERGY↗
NASA NTRS2010

Evolving the Technical Infrastructure of the Planetary Data System for the 21st Century

The Planetary Data System (PDS) was established in 1989 as a distributed system to assure scientific oversight. Initially the PDS followed guidelines recommended by the National Academies Committee on Data Management and Computation (CODMAC, 1982) and placed emphasis on archiving validated datasets. But overtime user demands, supported by increased computing capabilities and communication methods, have placed increasing demands on the PDS. The PDS must add additional services to better enable scientific analysis within distributed environments and to ensure that those services integrate with existing systems and data. To face these challenges the Planetary Data System (PDS) must modernize its architecture and technical implementation. The PDS 2010 project addresses these challenges. As part of this project, the PDS has three fundamental project goals that include: (1) Providing more efficient client delivery of data by data providers to the PDS (2) Enabling a stable, long-term usable planetary science data archive (3) Enabling services for the data consumer to find, access and use the data they require in contemporary data formats. In order to achieve these goals, the PDS 2010 project is upgrading both the technical infrastructure and the data standards to support increased efficiency in data delivery as well as usability of the PDS. Efforts are underway to interface with missions as early as possible and to streamline the preparation and delivery of data to the PDS. Likewise, the PDS is working to define and plan for data services that will help researchers to perform analysis in cost-constrained environments. This presentation will cover the PDS 2010 project including the goals, data standards and technical implementation plans that are underway within the Planetary Data System. It will discuss the plans for moving from the current system, version PDS 3, to version PDS 4.

Beebe, Reta F.↗
NASA NTRS2005

Designing and Implementing a Distributed System Architecture for the Mars Rover Mission Planning Software (Maestro)

Distributed systems allow scientists from around the world to plan missions concurrently, while being updated on the revisions of their colleagues in real time. However, permitting multiple clients to simultaneously modify a single data repository can quickly lead to data corruption or inconsistent states between users. Since our message broker, the Java Message Service, does not ensure that messages will be received in the order they were published, we must implement our own numbering scheme to guarantee that changes to mission plans are performed in the correct sequence. Furthermore, distributed architectures must ensure that as new users connect to the system, they synchronize with the database without missing any messages or falling into an inconsistent state. Robust systems must also guarantee that all clients will remain synchronized with the database even in the case of multiple client failure, which can occur at any time due to lost network connections or a user's own system instability. The final design for the distributed system behind the Mars rover mission planning software fulfills all of these requirements and upon completion will be deployed to MER at the end of 2005 as well as Phoenix (2007) and MSL (2009).

Goldgof, Gregory M.↗
NASA NTRS2021

Concept of Operations for OSIRIS-REx Optical Navigation Image Planning

Optical navigation (OpNav) is a critical subsystem of the OSIRIS-REx asteroid sample return mission, which operated in the vicinity of near-Earth asteroid (101955) Bennu from August 2018 through April 2021. A substantial amount of mission resources across multiple subsystems and institutions is required to ensure that the OpNav data are successfully acquired. The KinetX OpNav team, part of the Flight Dynamics System (FDS), is responsible for performing required analysis to develop the OpNav operations plans; requesting, reviewing and verifying the plans; and ultimately using the image data for critical navigation operations. The FDS team, responsible for the mission navigation, is operated by KinetX Aerospace with management and operations support from NASA’s Goddard Space Flight Center. The Science Processing and Operations Center (SPOC), located at the University of Arizona’s Lunar and Planetary Laboratory, is responsible for generating the planning products for all science and most OpNav data. These plans are integrated into the spacecraft sequences, tested, and commanded by the Mission Support Area (MSA) at Lockheed Martin Space. To ensure mission-critical navigation image data are successfully acquired, the plan is developed through a waterfall of planning cycles over the course of 3 months prior to onboard plan execution. During the initial strategic planning for a mission phase, detailed analysis is performed by the OpNav team to conceptualize the concept of operations (ConOps) for image data collection. This phase OpNav Narrative is included along with other strategic planning documents for the key ground segment stakeholders to review and provide feedback. The detailed OpNav plans get defined in the tactical planning cycle, which spans 8 to 3 weeks before the week-long integrated sequence is executed on-board the spacecraft. During the tactical cycle, the initial OpNav Request is submitted along with the science requests, kicking off development of the science and OpNav plans. Once the initial plan is drafted, interfaces are exercised so that the plan can be reviewed and iterated, if necessary. A rigorous schedule is followed by the planning teams during the implementation cycle, spanning the last 18 days before uplink, to ensure all the necessary integration, testing, and reviewing can occur on time. The development of the OpNav planning ConOps, including responsibilities, interfaces, timelines, and procedures, took extensive collaboration across mission elements and institutions. The process was robust throughout the 137 weeks of continuous Optical Navigation Operations at Bennu, which concluded on April 9th, 2021.

Coralie D. Adam↗
DOE OSTI2021

Current Status of Radiopharmaceutical Therapy

In radiopharmaceutical therapy (RPT), a radionuclide is systemically or locally delivered with the goal of targeting and delivering radiation to cancer cells while minimizing radiation exposure to untargeted cells. Examples of current RPTs include thyroid ablation with the administration of {sup 131}I, treatment of liver cancer with {sup 90}Y microspheres, the treatment of bony metastases with {sup 223}Ra, and the treatment of neuroendocrine tumors with {sup 177}Lu-DOTATATE. New RPTs are being developed where radionuclides are incorporated into systemic targeted therapies. To assure that RPT is appropriately implemented, advances in targeting need to be matched with advances in quantitative imaging and dosimetry methods. Currently, radiopharmaceutical therapy is administered by intravenous or locoregional injection, and the treatment planning has typically been implemented like chemotherapy, where the activity administered is either fixed or based on a patient’s body weight or body surface area. RPT pharmacokinetics are measurable by quantitative imaging and are known to vary across patients, both in tumors and normal tissues. Therefore, fixed or weight-based activity prescriptions are not currently optimized to deliver a cytotoxic dose to targets while remaining within the tolerance dose of organs at risk. Methods that provide dose estimates to individual patients rather than to reference geometries are needed to assess and adjust the injected RPT dose. Accurate doses to targets and organs at risk will benefit the individual patients and decrease uncertainties in clinical trials. Imaging can be used to measure activity distribution in vivo, and this information can be used to determine patient-specific treatment plans where the dose to the targets and organs at risk can be calculated. The development and adoption of imaging-based dosimetry methods is particularly beneficial in early clinical trials. In this work we discuss dosimetric accuracy needs in modern radiation oncology, uncertainties in the dosimetry in RPT, and best approaches for imaging and dosimetry of internal radionuclide therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2001

Chandra Space Flight Software: Using Software to Autonomously Operation the Largest and Most Sensitive X-Ray Telescope in the World

Chandra is the world's largest and most sensitive X-ray telescope. The Chandra X-ray Observatory is the third in NASA's family of "Great Observatories." The Chandra X-ray Observatory, launched by Space Shuttle Columbia on July 23, 1999, is NASA's newest Great Observatory. The Chandra space flight software is the operational software, which controls and directs the Chandra X-ray Observatory. The Chandra flight software has executed faultlessly for over 13,000 hours on-orbit. The Chandra flight software directly controls the Pointing, Aspect Determination, Electrical Power Subsystem, Propulsion system, and the Command, Communications, and Data Management subsystems. The software controls the spacecraft operations during all phases of the mission. The software also performs thermal control of the telescope to maintain pointing accuracy and monitors radiation levels throughout the orbit so that the Science Instruments can be safed if radiation thresholds are exceeded. The efficient operation of Chandra flight software has enabled the gathering of crucial science data. The Chandra flight software fault protection is the key to early detection and prevention of science instrument or spacecraft damage in an operating platform/environment, which is completely unforgiving. Permanently open Sun Shade Door and ACIS focal plane radiator sensitivity exposes science instruments and mirrors to damage for pointing anomalies causing an attitude excursion. The Chandra flight software must prevent these attitude excursions from occurring for ANY failure. Another example is that the power system has an unregulated bus, which imposes severe operating requirements on Chandra flight software to control array pointing and battery connection/disconnect using a unique algorithmic and logic approach. The Chandra flight software has enabled a truly autonomous vehicle with greater than 99% of all mission data collected as planned. Less than 15% of spacecraft operations are conducted in view (1 hour out of 8) leading to very extended periods without ground contact. The Chandra flight software implements the flexible mission plan during this out of view period, manages the solid state recorder capacity, controls all pointing and maneuvers, provides fault detection for all satellite subsystems, and initiates communications with the ground at the appropriate time. This paper will describe the software architecture features, key design elements and software testing techniques that have facilitated Chandra's success.

Crumbley, Tim↗
NASA NTRS2009

The Integrated Medical Model: A Risk Assessment and Decision Support Tool for Space Flight Medical Systems

The Integrated Medical Model (IMM) is a decision support tool that is useful to mission planners and medical system designers in assessing risks and designing medical systems for space flight missions. The IMM provides an evidence based approach for optimizing medical resources and minimizing risks within space flight operational constraints. The mathematical relationships among mission and crew profiles, medical condition incidence data, in-flight medical resources, potential crew functional impairments, and clinical end-states are established to determine probable mission outcomes. Stochastic computational methods are used to forecast probability distributions of crew health and medical resource utilization, as well as estimates of medical evacuation and loss of crew life. The IMM has been used in support of the International Space Station (ISS) medical kit redesign, the medical component of the ISS Probabilistic Risk Assessment, and the development of the Constellation Medical Conditions List. The IMM also will be used to refine medical requirements for the Constellation program. The IMM outputs for ISS and Constellation design reference missions will be presented to demonstrate the potential of the IMM in assessing risks, planning missions, and designing medical systems. The implementation of the IMM verification and validation plan will be reviewed. Additional planned capabilities of the IMM, including optimization techniques and the inclusion of a mission timeline, will be discussed. Given the space flight constraints of mass, volume, and crew medical training, the IMM is a valuable risk assessment and decision support tool for medical system design and mission planning.

Kerstman, Eric↗
DOE OSTI2017

Identifying requisite changes to support modern maintenance practices - 294

Modern data science and processing require the use of modern maintenance records. With traditional record-keeping practices, the isolated nature of the data, as well as the presence of handwritten maintenance work orders and incomplete service logs, severely limit the amount of progress that can be made towards advanced condition-based maintenance. These condition-based methods of diagnostics, prognostics, and maintenance planning are a primary goal of next-generation plant operations and maintenance strategies, and are all dependent on the availability of quality plant data including maintenance data. By implementing changes to maintenance record-keeping practices, such as the use of a computerized maintenance management system, key data will become available to allow for implementation of state-of-the-art data analysis methods. This paper covers the benefits of utilizing digital record systems to increase maintenance data availability in terms of condition-based maintenance and asset management. The ability of these methods to enable state-of-the-art practices within industrial organizations is discussed to offer insight into ways that these improvements to operating strategy will contribute to improvements in world-class maintenance excellence. A discussion of the future of asset management in terms of data analytics, maintenance decision support, and data integration is provided to illustrate the changing landscape of plant management. The result is an examination of innovative maintenance strategies and discussion of challenges related to the future of asset management and implementing modern maintenance practices. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Implementation of spot scanning dose optimization and dose calculation for helium ions in Hyperion

Purpose: Helium ions ({sup 4}He) may supplement current particle beam therapy strategies as they possess advantages in physical dose distribution over protons. To assess potential clinical advantages, a dose calculation module accounting for relative biological effectiveness (RBE) was developed and integrated into the treatment planning system Hyperion. Methods: Current knowledge on RBE of {sup 4}He together with linear energy transfer considerations motivated an empirical depth-dependent “zonal” RBE model. In the plateau region, a RBE of 1.0 was assumed, followed by an increasing RBE up to 2.8 at the Bragg-peak region, which was then kept constant over the fragmentation tail. To account for a variable proton RBE, the same model concept was also applied to protons with a maximum RBE of 1.6. Both RBE models were added to a previously developed pencil beam algorithm for physical dose calculation and included into the treatment planning system Hyperion. The implementation was validated against Monte Carlo simulations within a water phantom using γ-index evaluation. The potential benefits of {sup 4}He based treatment plans were explored in a preliminary treatment planning comparison (against protons) for four treatment sites, i.e., a prostate, a base-of-skull, a pediatric, and a head-and-neck tumor case. Separate treatment plans taking into account physical dose calculation only or using biological modeling were created for protons and {sup 4}He. Results: Comparison of Monte Carlo and Hyperion calculated doses resulted in a γ{sub mean} of 0.3, with 3.4% of the values above 1 and γ{sub 1%} of 1.5 and better. Treatment plan evaluation showed comparable planning target volume coverage for both particles, with slightly increased coverage for {sup 4}He. Organ at risk (OAR) doses were generally reduced using {sup 4}He, some by more than to 30%. Improvements of {sup 4}He over protons were more pronounced for treatment plans taking biological effects into account. All OAR doses were within tolerances specified in the QUANTEC report. Conclusions: The biological {sup 4}He model proposed above is a first approach matching biological data published so far. The advantage of {sup 4}He seems to lie in the reduction of dose to surrounding tissue and to OARs. Nevertheless, additional biological experiments and treatment planning studies with larger patient numbers and more tumor indications are necessary to study the possible benefits of helium ion beam therapy in detail.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results indicate that GPU-based PSO with further software optimization can make such planning clinically feasible. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

Implementing a Sleep Health Education and Sleep Disorders Screening Program in Fire Departments

The lost productivity from fatigue adds something like $2,000 per worker per year to an organization's overall costs. The human and financial burden of fatigue has prompted specialists to develop fatigue risk management plans, which have been implemented with success in industries like aviation, manufacturing, and first responders. We'll explore the common risk factors for fatigue and delve into successful programs from major players in the American marketplace, and provide actionable recommendations for change within your organization to prevent fatigue and encourage greater holistic wellness among your workers. Learning Objectives - Explain the causes and effects of the fatigue problem - Illustrate the difference between physical and chronic sleep-related fatigue - Explore fatigue management case studies from aviation, manufacturing, and warehousing - Demonstrate fatigue conclusions from research conducted at NASA - Answer common questions on fatigue management - Share recommendations from our experts on creating a fatigue management plan.

alertness↗