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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 OSTI2023

Method to Investigate Cognitive Aging Effects in Nuclear Operations Using the Rancor Microworld Simulator

Many nuclear power plant (NPP) operators are close to, or at retirement age. Since NPP operators are responsible for tasks that engage different and complex cognitive abilities, human factors researchers recently called for the need to examine cognitive aging effects and potential interactions with modernized control rooms. One way control rooms are being modernized is by replacing paper-based procedures with computer-based procedures (CBPs). This paper outlines a method for testing preference and usability of three types of CBPs as a function of age, in simulated NPP operations. The Rancor Microworld Simulator will be used, which mimics an NPP control room, allowing novices to learn to perform operator tasks quickly. The experiment will have two independent variables: age (young and old) and CBP-type (Type 1, Type 2, and Type 3). The three types of CBPs vary in levels of embedded instrumentation and controls. Adults aged 18–25 years (young group) and =60 years (older group) will be recruited from the general population. The dependent variables will be preference and usability metrics via survey and simulator logs. All participants will perform two scenario types (startup and loss of feedwater operations) and will be randomly assigned to one type of CBP to guide them through the scenario. Analyses of variance (ANOVAs) will be used to determine whether there is a main effect of age and any age x CBP-type interactions on the preference and usability dependent variables.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

42 ENGINEERING↗
NASA NTRS1976

Conference on In Situ Composites, 2nd, Bolton Landing, N.Y., September 2-5, 1975, Proceedings

The present collection of papers deals with advances in several areas of interest in in-situ composites. Major areas discussed include solidification and structural control, physical properties, mechanical behavior, stability, environmental response, and development of new metallic and ceramic systems. New evidence about directional solidification in terms of temperature gradients is presented along with solidification rates for successful formation of in-situ composite structures. Much is learned about the factors that control structure in directionally solidified eutectic alloys and the relationship of properties to structure, especially the change in properties as a function of operating environment. Less progress is achieved in the area of physical properties than in the area of mechanical properties. Individual items are announced in this issue.

Jackson, M. R.↗
DOE OSTI2025

Adaptive PID Gain Scheduling Control for Hydropower Turbine Using Neural CDE and Stochastic Distribution Shaping

This paper introduces a gain-scheduling PID controller design strategy for hydroturbine frequency control mode. This scheme first uses real data to learn the nonlinear dynamics of the hydroturbine using neural controlled differential equations and then perturbs the obtained nonlinear system at different equilibrium points, based on which a static output feedback adaptive dynamic programming algorithm is then used to optimize the PID gains for each equilibrium point. Moreover, a continuous-time version of stochastic distribution control is proposed to further fine-tune the optimized PID gains. Finally, the controller is obtained by implementing linear interpolation between the optimized PID control gains. The simulation results show that the proposed gain-scheduling PID controller can control a larger range of operation points compared with the given fixed PID controller and the baseline method. Compared with the given fixed PID controller, the proposed gain-scheduling PID controller can regulate hydroturbine frequency against disturbances induced by power-load variation with over 50% less overshoot for some operation points.

13 HYDRO ENERGY↗
NASA NTRSDate not supplied

Manual Crew Override of Vehicle Landings Following G-Transitions

BACKGROUND Manual control during exploration spaceflight consists of both planned automated supervisory control and unplanned crew override. This crew override capability is critical to enable overall mission success during landing contingencies. However, the introduction of manual override capabilities must be implemented to enable crews to mitigate risks introduced by human error. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. While training and landing aids enable successful landing through disorientation, these adaptive changes may increase cognitive demand that needs to be accounted for in the manual control strategy. It is important to characterize these effects as soon as possible following the G-transition to develop appropriate countermeasures. METHODS The following study seeks to inform the risk associated with altered sensorimotor and vestibular function impacting critical mission tasks. We aim to characterize the effects of short and long-duration weightlessness on manual control following G-transitions using simulated lunar landing on a six-degree-of-freedom (6DOF) motion base, a fixed base simulation, and a supervisory control tablet task. The primary goal is to understand the impact of spaceflight on crew ability to perform manual crew override and supervisory control. This aim will be assessed by comparing pre- versus postflight simulation performance in crewmembers assigned to either short duration (< 30 day) or long duration (~6- month) missions to the International Space Station (ISS). We hypothesize there will be postflight increases in the percent time that pilots are outside of the acceptable range for recommended vehicle state parameters and the reaction time for secondary cognitive tasks. Ground-based control subjects, who are demographically matched to the crew considering age (± 5 years) and gender, will undergo the same testing schedule as the crew to examine the effects of flight phase independent of microgravity exposure. The second aim is to examine how adaptive changes in vestibular and cognitive function relate to changes in manual crew override proficiency. Crew performance for a sensorimotor perceptual test battery will evaluate motion perception tracking, roll nulling, and/or vection sensitivity using the 6DOF motion base. We hypothesize that a higher severity of vestibular alterations will be associated with increased percent time outside of guidance limits. Motion sickness severity and sleepiness will also be evaluated. To determine the impact of “just-in-time” training, the third aim seeks to compare performance during on-board lunar landing tasks conducted late in-flight to early postflight. We hypothesize that proficiency on the “just-in-time” laptop trainer late in mission will be positively correlated with early postflight proficiency on the same task. The final aim will establish assessments of performance, training protocols, and the learning progression in a ground-control cohort of first-time users. RESULTS The assessment of the learning progression associated with the piloting task on the motion base system with thirty ground subjects will be reported. Learning curves will be established across four distinct sessions and within session considering trial difficulty. The difficulty of the landing task can be modulated with the landing divert distance and cross or downrange difficulty. Results may include changes in performance across multiple trials of a multi-attribute lunar tablet supervisory control task. Preliminary investigations of eighteen subjects who completed vestibular threshold and motion perception tasks offer expected performance ranges for upcoming preflight crew evaluations. The results yielded an average roll threshold of 0.46 ± 0.30 deg/s and an average roll nulling root mean square error performance of 2.52 ± 0.52 deg/s. RELEVANCE This project will deliver an operational demonstration of crew monitoring capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew performance will help better characterize the manual control risks associated with sensorimotor alterations. Ground testing will evaluate learning progression, refine training protocols, and serve as a control cohort for comparisons to crew performance. ACKNOWLEDGEMENTS: The authors acknowledge contributions from Draper, the Dynamic Skills Trainer (DST) Lab, and the Software, Robotics, and Simulation Division toward the development of the lunar landing simulation platforms. This project is funded by the Human Health Countermeasures Element.

Hannah M. Weiss↗
NASA NTRS2000

Human orientation and movement control in weightless and artificial gravity environments

Our goal is to summarize what has been learned from studies of human movement and orientation control in weightless conditions. An understanding of the physics of weightlessness is essential to appreciate the dramatic consequences of the absence of continuous contact forces on orientation and posture. Eye, head, arm, leg, and whole body movements are discussed, but only experiments whose results seem relatively incontrovertible are included. Emphasis is placed on distinguishing between virtually immediate adaptive compensations to weightlessness and those with longer time courses. The limitations and difficulties of performing experiments in weightless conditions are highlighted. We stress that when astronauts and cosmonauts return from extended space flight they do so with both physical "plant" and neural "controller" structurally and functionally altered. Recent developments in adapting humans to artificial gravity conditions are discussed as a way of maintaining sensory-motor and structural integrity in extended missions involving transitions between different force environments.

NASA Discipline Neuroscience↗
DOE OSTI2018

Study of MHD Stability, Active Mode Control, and Disruption Avoidance in NSTX-U

Learning how to achieve sustained operation at high plasma pressure is an important goal of the National Spherical Torus Upgrade (NSTX-U) program and is critically important to any future fusion power plant. This research addresses high priority stability and control issues outlined by the NSTX-U Program. Reduced models of the stabilization physics learned continue to be created and tested against actual plasma operation in NSTX, NSTX-U, and other tokamak devices. This research is forming physics-based models for disruption forecasting that aims to unify results across different fusion machines for more confident extrapolation of the techniques studied to future devices including ITER. The Disruption Event Characterization and Forecasting (DECAF) code was expanded to analyze data from the TCV tokamak, including real-time MHD analysis signals. The presence of a mode and likelihood of its m/n numbers can be tracked, as well as the mode frequency, although a limitation at low frequency does not yet allow tracking modes to the locking point in TCV. Initial analyses of NSTX-U operation were conducted using the DECAF code. The timing of sawtooth oscillations was investigated taking advantage of the more extensive L-mode operation of the device in its first run campaign. An initial investigation of thermal collapses in NSTX-U was also conducted with specific comparison to the Greenwald density limit. A local island power balance model to explain the density limit in tokamaks has been tested with NSTX data and is being evaluated as a potential disruption forecaster in the DECAF code, presently showing similarities to the global Greenwald density evolution and limit. A module was written for the DECAF code that generalizes magnetic spectrogram analysis of MHD modes. This new portable code, written in Python, discriminates the toroidal mode number of instabilities and can track each mode as a DECAF event, monitoring the evolution of the mode through bifurcation and locking points. This is a critical new capability to allow disruption event characterization for general tokamak databases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Automated Control of Transactive HVACs in Energy Distribution Systems

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. Here, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user comfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Safety-Related Instrumentation and Control Upgrade Pilot Project: NUREG- 0711 Process Development, Planning and Analysis Activities, and Lessons Learned

Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper presents the development execution of the HFE Program through completion of the Planning and Analysis Phase as defined by NUREG-0711 as well as HFE lessons learned.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2011

Category Learning Research in the Interactive Online Environment Second Life

The interactive online environment Second Life allows users to create novel three-dimensional stimuli that can be manipulated in a meaningful yet controlled environment. These features suggest Second Life's utility as a powerful tool for investigating how people learn concepts for unfamiliar objects. The first of two studies was designed to establish that cognitive processes elicited in this virtual world are comparable to those tapped in conventional settings by attempting to replicate the established finding that category learning systematically influences perceived similarity . From the perspective of an avatar, participants navigated a course of unfamiliar three-dimensional stimuli and were trained to classify them into two labeled categories based on two visual features. Participants then gave similarity ratings for pairs of stimuli and their responses were compared to those of control participants who did not learn the categories. Results indicated significant compression, whereby objects classified together were judged to be more similar by learning than control participants, thus supporting the validity of using Second Life as a laboratory for studying human cognition. A second study used Second Life to test the novel hypothesis that effects of learning on perceived similarity do not depend on the presence of verbal labels for categories. We presented the same stimuli but participants classified them by selecting between two complex visual patterns designed to be extremely difficult to label. While learning was more challenging in this condition , those who did learn without labels showed a compression effect identical to that found in the first study using verbal labels. Together these studies establish that at least some forms of human learning in Second Life parallel learning in the actual world and thus open the door to future studies that will make greater use of the enriched variety of objects and interactions possible in simulated environments compared to traditional experimental situations.

Andrews, Jan↗
NASA NTRS2024

Optimizing Air Traffic - Integrating Artificial Intelligence and Machine Learning in Flight Path Planning and 3D Airspace Visualization for Air Traffic Control

Air Traffic Control (ATC) systems are vital components of the National Airspace System (NAS). ATC, Airport Traffic Control Towers (ATCT), and Terminal Radar Approach Control (TRACON) are responsible for directing all flights departing from and arriving at airports, managing our nation’s airspace, preventing potential accidents, and ensuring that every flight is accounted for. However, these systems often face challenges in effectively monitoring the skies. Issues such as poor communication between operators, difficulty in performing operations, and the constant need for vigilance frequently burden ATC operators. Additionally, the projected increase in air traffic in the coming years will only exacerbate the stress associated with this role. To address these issues, we propose a system that assists ATC operators in situations such as handovers, emergencies, and routing aircraft to avoid weather hazards. Our solution includes an Artificial Intelligence (AI) and Machine Learning (ML)-based Flight Pathways Planning System (FPPS) designed to find the fastest and most optimal routes for aircraft, taking into account weather conditions, restricted terrain, and Extended-Range Twin-Engine Operational Performance Standards (ETOPS) ratings. The proposed Predictive Weather Planning Model, included in FPPS, adjusts routes based on real-time and forecasted weather conditions. Additionally, our NVIDIA Omniverse 3D Visualization System offers a highly interactive environment for better visualization and a clear view of the airspace. By incorporating these systems, the roles of ATC, ATCT, and TRACON operators will become more manageable and less stressful, equipping them to efficiently handle the growing density of airspace.

Regina Ayoubi↗
NASA NTRS2005

Investigations of Desert Dust and Smoke in the North Atlantic in Support of the TOMS Instrument

During the initial period of the work we concentrated on Saharan dust storms and published a sequence of papers (Colarco et a1 2002,2003a,b, Toon, 2004). The U.S. Air Force liked the dust model so well that they appropriated it for operational dust storm forecasting (Barnum et al., 2004). The Air Force has used it for about 5 yrs in the Middle East where dust storms cause significant operational problems. The student working on this project, Peter Colarco, has graduated and is now a civil servant at Goddard where he continues to interact with the TOMS team. This work helped constrain the optical properties of dust at TOMS wavelengths, which is useful for climate simulations and for TOMS retrievals of dust properties such as optical depth. We also used TOMS data to constrain the sources of dust in Africa and the Middle East, to determine the actual paths taken by Saharan dust storms, to learn more about the mechanics of variations in the optical depths, and to learn more about the mechanisms controlling the altitudes of the dust. During the last two years we have been working on smoke from fires. Black carbon aerosols are one of the leading factors in radiative forcing. The US Climate Change Science Program calls this area out for specific study. It has been suggested by Jim Hansen, and Mark Jacobsen among others, that by controlling emissions of black carbon we might reduce greenhouse radiative forcing in a relatively painless manner. However, we need a greatly improved understanding of the amount of black carbon in the atmosphere, where it is located, where it comes from, how it is mixed with other particles, what its actual optical properties are, and how it evolves. In order to learn about these issues we are using a numerical model of smoke. We have applied this model to the SAFARI field program data, and used the TOMS satellite observations in that period (Sept. 2000). Our goal is to constrain source function estimates for black carbon, and smoke optical properties.

Toon, Owen B.↗
NASA NTRS1995

Robust integrated neurocontroller for complex dynamic systems

The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system; (2) Development of a robust neurocontroller system, and; (3) Integration of above systems to create a robust Integration Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.

Zein-Sabbato, S.↗
NASA NTRS1996

Robust Integrated Neurocontroller for Complex Dynamic Systems

The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are: adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as: sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system, (2) Development of a robust neurocontroller system, and 3. Integration of above systems to create a Robust Integrated Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.

Zein-Sabatto, S.↗
DOE OSTI2019

Target Control and Protection Systems Lessons from SNS Operations

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory has been in operations since 2006 and proposes a project to build a Second Target Station (STS) to effectively double potential scientific output. The SNS target controls operate in a harsh environment which includes high radiation, exposure to gaseous radionuclides, and activated liquid mercury and mercury vapor. These conditions necessitate protective interlocks and credited controls for protection functions to ensure proper response to off-normal conditions. In order to inform the design of target controls for the STS, we have examined lessons learned during SNS operations regarding the design and implementation of the control and protection systems for the first target station (FTS). This paper will examine various aspects of the performance of the target control and protection systems including reliability, maintainability and sustainability given the challenging environment created by 1.4 MW operations. Specific topics include distributed control of various target subsystems, response to loss of power, selection of nuclear grade instrumentation, and applying these lessons to the design for the STS project.

Humphreys, Dana↗
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↗
NASA NTRS2008

Space Transportation Systems Life Cycle Cost Assessment and Control

Civil and military applications of space transportation have been pursued for just over 50 years and there has been, and still is, a need for safe, dependable, affordable, and sustainable space transportation systems. Fully expendable and partially reusable space transportation systems have been developed and put in operation that have not adequately achieved this need. Access to space is technically achievable, but presently very expensive and will remain so until there is a breakthrough in the way we do business. Since 1991 the national Space Propulsion Synergy Team (SPST) has reviewed and assessed the lessons learned from the major U.S. space programs of the past decades focusing on what has been learned from the assessment and control of Life Cycle Cost (LCC) from these systems. This paper presents the results of a selected number of studies and analyses that have been conducted by the SPST addressing the need, as well as the solutions, for improvement in LCC. The major emphasis of the SPST processes is on developing the space transportation system requirements first (up front). These requirements must include both the usual system flight performance requirements and also the system functional requirements, including the infrastructure on Earth's surface, in-space and on the Moon and Mars surfaces to determine LCC. This paper describes the development of specific innovative engineering and management approaches and processes. This includes a focus on flight hardware maturity for reliability, ground operations approaches, and business processes between contractor and government organizations. A major change in program/project cost control is being proposed by the SPST to achieve a sustainable space transportation system LCC - controlling cost as a program metric in addition to the existing practice of controlling performance and weight. Without a firm requirement and methodically structured cost control, it is unlikely that an affordable and sustainable space transportation system LCC will ever be achieved. '

Robinson, John W.↗