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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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NASA NTRS2019

Air Traffic Management Technology Demonstration-1 (ATD-1) Avionics Phase 2 Flight Test Training for Interval Management

Prior to the successful flight test validation of a new avionics prototype, participants from Boeing, Honeywell, and United Airlines underwent group training at NASA Langley Research Center. New prototype software for an algorithm which enables greater efficiency in high-density airspace, called Interval Management, was to be incorporated into Electronic Flight Bags and placed in the cockpit for pilot usage. The goals of the training were to teach the flight test pilots how to operate the new software, establish techniques to simultaneously position three aircraft prior to each test scenario, and ensure a common communication protocol among team members when coordinating the position of aircraft for the next scenario. The multi-tiered interactive training regimen consisted of a process that continually built upon previous foundational material. The primary learning elements were 1) a portable computer-based trainer that was provided to the pilots prior to classroom training sessions, 2) classroom learning, 3) full mock-up simulator training, and 4) refresher training just prior to the flight test. Each part of the regimen was designed to repeat and build upon the previous element. The purpose of this Technical Memorandum is to inform the aviation industry how flight training for Interval Management was conducted at Langley Research Center in order to reduce overall development costs of future Interval Management training programs. Secondly, the paper provides insight regarding the decision-making process when attempting to conduct a flight test.

Roper, Roy D.↗
DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Long-term Engagement in Authentic Research with NASA (LEARN): Innovative Practices Suggested by a New Model for Teacher Research Experiences

NASA's LEARN Project is an innovative program that provides long-term immersion in the practice of atmospheric science for middle and high school in-service teachers. Working alongside NASA scientists and using authentic NASA Science Mission Directorate research data, teachers develop individual research topics of interest during two weeks in the summer while on-site at NASA Langley. With continued, intensive mentoring by NASA scientists, the teachers further develop their research throughout the academic year through virtual group meetings and data team meetings mirroring scientific collaborations. At the end of the first year, LEARN teachers present scientific posters. The LEARN experience has had such an impact that multiple teachers from the first two cohorts have elected to continue their research. The LEARN project evaluation has provided insights into particularly effective elements of this new approach. Findings indicate that teachers? perceptions of the scientific enterprise have changed, and that LEARN provided substantial resources to help them take real-world research to their students. This presentation will focus on key factors from LEARN?s implementation that inform best practices for the incorporation of authentic scientific research into teacher professional development experiences. We suggest that these factors should be considered in the development of other such experiences, including: (1) The involvement of a single scientist as both the project leader/manager and the project scientist, to ensure that the project can meet teachers? needs. (2) An emphasis on framing and approaching scientific research questions, so that teachers can learn to evaluate the feasibility of studies based on scope, scale, and availability of data. (3) Long term, ongoing relationships where teachers and scientists work as collaborators, beyond the workshop ?mold.? (4) A focus on meeting the needs of individual teachers, whether their needs relate to elements of research and analysis, or to their tight professional schedules. (5) Above all, flexibility and patience. LEARN builds relationships with teachers slowly, over a long period of time. In the middle, life often intervenes. LEARN has emphasized that teachers? success is more important than deadlines or following a rigid protocol.

M Pippin↗
NASA NTRS1990

Recent developments in learning control and system identification for robots and structures

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

Phan, M.↗
NASA NTRS2001

Lessons Learned and Technical Standards: A Logical Marriage

A comprehensive database of lessons learned that corresponds with relevant technical standards would be a boon to technical personnel and standards developers. The authors discuss the emergence of one such database within NASA, and show how and why the incorporation of lessons learned into technical standards databases can be an indispensable tool for government and industry. Passed down from parent to child, teacher to pupil, and from senior to junior employees, lessons learned have been the basis for our accomplishments throughout the ages. Government and industry, too, have long recognized the need to systematically document And utilize the knowledge gained from past experiences in order to avoid the repetition of failures and mishaps. The use of lessons learned is a principle component of any organizational culture committed to continuous improvement. They have formed the foundation for discoveries, inventions, improvements, textbooks, and technical standards. Technical standards are a very logical way to communicate these lessons. Using the time-honored tradition of passing on lessons learned while utilizing the newest in information technology, the National Aeronautics and Space Administration (NASA) has launched an intensive effort to link lessons learned with specific technical standards through various Internet databases. This article will discuss the importance of lessons learned to engineers, the difficulty in finding relevant lessons learned while engaged in an engineering project, and the new NASA project that can help alleviate this difficulty. The article will conclude with recommendations for more expanded cross-sectoral uses of lessons learned with reference to technical standards.

Gill, Paul↗
NASA NTRS1992

Space Station Freedom Gateway to the Future

The first inhabited outpost on the frontier of space will be a place to live, work, and discover. Experiments conducted on Freedom will advance scientific knowledge about our world, our environment, and ourselves. We will learn how to adapt to the space environment and to build and operate new spacecraft with destinations far beyond Earth, continuing the tradition of exploration that began with a journey to the Moon. What we learn from living and working on Freedom will strengthen our expertise in science and engineering, promote national research and development initiatives and inspire another generation of Americans to push forward and onward. On the eve of the 21st century, Space Station Freedom will be our gateway to the future. This material covers gateways to space, research, discovery, utilization, benefits, and NASA.

Source record↗
NASA NTRS2015

Johnson Space Center's Free Range Bicycle Program.- Fall 2015 Intern Report

NASA's Johnson Space Center is a big place, encompassing 1,620 acres and more than a hundred buildings. Furthermore, there are reportedly 15 thousand employees, all of which have somewhere to be. To facilitate the movement of all these people JSC has historically relied on human power. Pedaling their way towards deep space, bicycles have been the go to method. Currently there are about 200 Free Range Bicycles at JSC. Free Range Bicycles belong to nobody, except NASA, and are available for anybody to use. They are not to be locked or hidden (although frequently are) and the intention is that there will always be a bike to hop on to get where you're going (although it may not be the bike you rode in on). Although not without its own shortcomings, the Free Range Bicycle Program has continued to provide low cost, simple transportation for NASA's JSC. In addition to the approximately 200 Free Range Bicycles, various larger divisions (like engineering) will often buy a few dozen bikes for their team members to use or individuals will bring their own personal bike to either commute or use on site. When these bicycles fall into disrepair or are abandoned (from retirees etc) they become a problem at JSC. They are an eye sore, create a safety hazard and make it harder to find a working bike in a time of need. The Free Range Program hopes to address this first problem by "tagging out" abandoned or out of service bicycles. A bright orange "DO NOT OPERATE" tag is placed on the bike and given a serial number for tracking purposes. See picture to the right. If the bike has an active owner with intentions to repair the bike the bottom of the tag has instructions for how to claim the abandoned bicycle. After being tagged the owner of the bicycle has 30 days to claim the bicycle and either haul it off site or get it repaired (and labeled) in accordance with Johnson's Bicycle Policy. If the abandoned bicycle is not claimed within 30 days it becomes the property of the Government. The bicycle is then (in short) repaired, labeled, documented and converted to a free range bicycle. Bikes beyond repair are cannibalized of useable parts and then scrapped. That was nearly the first thing I did when arriving at work. After getting settled in and coordinating the purchase of 50 new bicycles (elaboration below); I started combing the Center. Bikes are hidden and tucked away in the oddest of places and it was my priority to root all of them out. I tagged 70 bikes on the center, setting a record. I tagged so many bikes I ran out tags and had to make more. Long ago it was discovered that the same harsh elements that wreak havoc on the bikes, destroy the tags before the 30 day timeframe. The tags are labeled with instructions on how to claim the bike and numbered, then these paper labels are laminated with packing tape. It sounds like a simple process but nothing fits right, everything has to be trimmed, put on straight and is generally a pain in the neck. So I made an assembly process and knocked out a few hundred to save having to do it again for a while. I walked the center and tagged bikes at every building hiding in nearly every nook and cranny. Thus, in conclusion, I've done many things here at JSC on my first term and had a blast doing it. I've learned a lot from my multi-faceted roles and continual challenges. In short I've done my best to get folks at JSC on bikes and keep folks at JSC on bikes and tended a flock of free range bicycles. Everything from turning the rusty bolts and oiling chains to coordinating a $20,000 deal on 50 new free range bicycles. I am proud of the bicycle shop I have built and am infinitely grateful to the people who lead, help, guide and support me. I have fixed a great number of bicycles and cleaned the center of unsightly and unsafe piles of bicycles. I am immensely thankful for this opportunity to learn and serve and appreciate the privilege of coming back for a second term. A second term, in which I will continue to develop this awesome program.

Lee-Stockton, Willem↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Learning from Past Missions for Today’s Case Studies

As interest in small spacecraft and its community continue to grow, the extensive selection of subsystem parts and service providers can present as many opportunities as unique challenges for mission design and implementation. These challenges often include inadequate trade studies, missing lessons learned, and unknown solutions to common pitfalls. Trade studies frequently fall short if products and services ultimately do not meet customer expectations, especially if the actual performance of a particular service or technology is not shared with others seeking similar products or services. It is difficult to quantify the viability and robustness of desired products and services, truly understand what solutions exist, and account for unique failures from previous SmallSat missions when experiences are not shared and captured. This noted, one reason for the lack of information as input for trade studies is that it is problematic to capture and disseminate the relevant lessons learned, experienced anomalies, and programmatic issues in both the laboratory and on-orbit setting, as these types of information can be extremely sensitive and are specific to each unique mission. Often, they are not made publicly available. The adoption and use of existing tools and databases, as well as contributions toward and sharing of information available in literature can help distribute useful information for future SmallSat missions to help avoid these common pitfalls. This paper will provide the required framework for current and future SmallSat mission implementation by providing best practices and identifying helpful resources to assist with subsystem parts and services as part of trade study selections.

Bruce Yost↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

The MCNP ® 6 code: A decade of progress

After several years of effort involved in merging the Los Alamos National Laboratory MCNP5 and MCNPX codes, in 2013 the first production release of version 6 of the Monte Carlo N-Particle ® , or MCNP ® , code MCNP6.1 was distributed publicly. Since then, three significant releases have been issued: MCNP6.1.1beta in 2014, MCNP6.2 in 2018, and MCNP6.3 in 2023. While each release always contains new features, code enhancements, and bug fixes, each version has had a different primary focus, ranging from improved calculational efficiency to new powerful utilities and tools, to software modernization of the code base. With all that has been learned over the first decade of the MCNP6 code, continuous progress is being made toward a modernized, general-purpose Monte Carlo radiation transport code that remains a trusted resource for the global community of practitioners. This paper describes these first 10+ years of the MCNP6 code and its continually improving data libraries, and gives some insight into how the next decade is expected to unfold.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Polarized target nuclear magnetic resonance measurements with deep neural networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

Metrology↗
DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
NASA NTRS1992

Scheduling lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler.

Ringer, Mark J.↗
NASA NTRS2002

Novel Advancements in Internet-Based Real-Time Data Technologies

AZ Technology has been working with NASA MSFC (Marshall Space Flight Center) to find ways to make it easier for remote experimenters (RPI's) to monitor their International Space Station (ISS) payloads in real-time from anywhere using standard/familiar devices. That effort resulted in a product called 'EZStream' which is in use on several ISS-related projects. Although the initial implementation is geared toward ISS, the architecture and lessons learned are applicable to other space-related programs. This paper begins with a brief history on why Internet-based real-time data is important and where EZStream or products like it fit in the flow of data from orbit to experimenter/researcher. A high-level architecture is then presented along with explanations of the components used. A combination of commercial-off-the-shelf (COTS), Open Source, and custom components are discussed. The use of standard protocols is shown along with some details on how data flows between server and client. Some examples are presented to illustrate how a system like EZStream can be used in real world applications and how care was taken to make the end-user experience as painless as possible. A system such as EZStream has potential in the commercial (non-ISS) arena and some possibilities are presented. During the development and fielding of EZStream, a lot was learned. Good and not so good decisions were made. Some of the major lessons learned will be shared. The development of EZStream is continuing and the future of EZStream will be discussed to shed some light over the technological horizon.

Myers, Gerry↗
NASA NTRS2006

Integrated System Health Management (ISHM) Technology Demonstration Project Final Report

Integrated System Health Management (ISHM) is an essential capability that will be required to enable upcoming explorations mission systems such as the Crew Exploration Vehicle (CEV) and Crew Launch Vehicle (CLV), as well as NASA aeronautics missions. However, the lack of flight experience and available test platforms have held back the infusion by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL) of ISHM technologies into future space and aeronautical missions. To address this problem, a pioneer project was conceived to use a high-performance aircraft as a low-cost proxy to develop, mature, and verify the effectiveness of candidate ISHM technologies. Given the similarities between spacecraft and aircraft, an F/A-18 currently stationed at Dryden Flight Research Center (DFRC) was chosen as a suitable host platform for the test bed. This report describes how the test bed was conceived, how the technologies were integrated on to the aircraft, and how these technologies were matured during the project. It also describes the lessons learned during the project and a forward path for continued work.

Mackey, Ryan↗
NASA NTRS2011

Reinventing the International Space Station Payload Integration Processes and Capabilities

The fundamental ISS payload integration philosophy, processes and capabilities were established in the context of how NASA science programs were conducted and executed in the early 1990 s. Today, with the designation of the United States (US) portion of ISS as a National Lab, the ISS payload customer base is growing to include other government agencies, private and commercial research. The fields of research are becoming more diverse expanding from the NASA centric physical, materials and human research sciences to test beds for exploration and technology demonstration, biology and biotechnology, and as an Earth and Space science platform. This new customer base has a broader more diverse set of expectations and requirements for payload design, verification, integration, test, training, and operations. One size fits all processes are not responsive to this broader customer base. To maintain an organization s effectiveness it must listen to its customers, understand their needs, learn from its mistakes, and foster an environment of continual process improvement. The ISS Payloads office is evolving to meet these new customer expectations.

Jones, Rod↗