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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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The topics covered include the following: defining research needs; understanding land use change; improving policy analysis -- research on the decision-making process; designing policy instruments and institutions to address energy-related environmental problems; assessing impacts, vulnerability, and adaptation to global changes; and understanding population dynamics and global change.
Computer-generated human models have been used in aerospace design for a decade. They have come to be highly reliable for worksite analysis of certain types of EVA tasks. In many design environments, this analysis comes after the structural design is largely complete. However, the use of these models as a development tool is gaining acceptance within organizations that practice good systems engineering processes. The design of the United States Propulsion Module for the International Space Station provides an example of this application. The Propulsion Module will provide augmentation to the propulsion capability supplied by the Russian Service Module Zvezda. It is a late addition to the set of modules provided by the United States to the ISS Program, and as a result, faces design challenges that result from the level of immaturity of its integration into the Station. Among these are heat dissipation and physical envelopes. Since the rest of the Station was designed to maximize the use of the cooling system, little margin is available for the addition of another module. The Propulsion Module will attach at the forward end of the Station, and will be between the Orbiter and the rest of ISS. Since cargo must be removed from the Payload Bay and transferred to Station by the Canadarm, there is a potential for protrusions from the module, such as thruster booms, to interfere with robotic operations. These and similar engineering issues must be addressed as part of the development. In the implementation of good system design, all design solutions should be analyzed for compatibility with all affected subsystems. Human modeling has been used in this project to provide rapid input to system trades of design concepts. For example, the placement of radiators and avionics components for optimization of heat dissipation had to be examined for feasibility of EVA translation paths and worksite development. Likewise, the location of and mechanism for the retraction of thruster booms was partly driven by available Orbiter, robotic arm, and other module envelopes; worksite analysis was required for early assessment of task success. Since these trade studies included the EVA analysis as part of the decision criteria, the design had a high degree of assurance of EVA supportability from the outset. This approach contributes greatly to mission success.
The Advanced Technology Lifecycle Analysis System (ATLAS) is a decision support tool designed to aid program managers and strategic planners in determining how to invest technology research and development dollars. It is an Excel-based modeling package that allows a user to build complex space architectures and evaluate the impact of various technology choices. ATLAS contains system models, cost and operations models, a campaign timeline and a centralized technology database. Technology data for all system models is drawn from a common database, the ATLAS Technology Tool Box (TTB). The TTB provides a comprehensive, architecture-independent technology database that is keyed to current and future timeframes.
The High Fidelity System Simulation effort has addressed various important objectives to enable additional capability within the NPSS framework. The scope emphasized High Pressure Turbine and High Pressure Compressor components. Initial effort was directed at developing and validating intermediate fidelity NPSS model using PD geometry and extended to high-fidelity NPSS model by overlaying detailed geometry to validate CFD against rig data. Both "feedforward" and feedback" approaches of analysis zooming was employed to enable system simulation capability in NPSS. These approaches have certain benefits and applicability in terms of specific applications "feedback" zooming allows the flow-up of information from high-fidelity analysis to be used to update the NPSS model results by forcing the NPSS solver to converge to high-fidelity analysis predictions. This apporach is effective in improving the accuracy of the NPSS model; however, it can only be used in circumstances where there is a clear physics-based strategy to flow up the high-fidelity analysis results to update the NPSS system model. "Feed-forward" zooming approach is more broadly useful in terms of enabling detailed analysis at early stages of design for a specified set of critical operating points and using these analysis results to drive design decisions early in the development process.
NASA's successful conduct of the Apollo Program greatly enhanced the prestige of the United States and remains broadly accepted as America's gift to all Mankind. NASA's accomplishments continue to amaze the world. With the Vision for Space Exploration (VSE) Americans once again tasked NASA to carry out a project that is expected to provide inspiration and economic stimulus to the United States and to the world. In preparation NASA has thoroughly examined space program precedents. There is, however, another precedent which has not been examined in this context but whose scope and environment in many ways parallel the VSE. This project was initiated by a team that had, ten years before, successfully completed an effort that, at a cost of $151 billion (2008 values), had pushed the envelope of technology, brought economic growth, established their country as the world leader in engineering, and been broadly accepted as their country s gift to Mankind. The new project was again inspired by popular desire to enhance national prestige and make yet another major contribution to Humanity. The new effort was predicted to require eight years and $156 billion (2008 values). However, after eight years and expenditures of 80% beyond the baseline, the project collapsed amid bankruptcy, political scandal, and criminal prosecution. This paper applies current aerospace project metrics, such as earned value analysis, to review the strategic decisions in this historic failure and describe its ultimate collapse. Key mistakes are identified, and lessons are drawn which may prove useful in guiding the VSE.
NASA's successful conduct of the Apollo Program greatly enhanced the prestige of the United States and remains broadly accepted as America's gift to all Mankind. NASA's accomplishments continue to amaze the world. With the Vision for Space Exploration (VSE) Americans once again tasked NASA to carry out a project that is expected to provide inspiration and economic stimulus to the United States and to the world. In preparation NASA has thoroughly examined space program precedents. There is, however, another precedent which has not been examined in this context but whose scope and environment in many ways parallel the VSE. This project was initiated by a team that had, ten years before, successfully completed an effort that, at a cost of $173 billion (in 2008 dollars), had pushed the envelope of technology, brought economic growth, established their country as the world leader in engineering, and been broadly accepted as that country's gift to all Mankind. The new project was again inspired by popular desire to enhance national prestige and make yet another major contribution to Humanity. This effort was predicted to require eight years and $156 billion (2008 dollars). However, after nine years and expenditures of 96% beyond the baseline, the project collapsed amid bankruptcy, political scandal, and criminal prosecution. This paper applies current project management metrics, such as earned value analysis, to review the strategic decisions in this historic failure and describe its ultimate collapse. Key mistakes are identified, and lessons are drawn which may prove useful in guiding the VSE.
Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.
Previous JPL ground systems have been designed with the Ground Data System (GDS) engineer in mind. The focus on these systems has been on packaging and delivery of low level information (frames, packets, telemetry values) to the end user. It was not that long ago when project teams would be huddled over a workstation, examining crude displays of telemetry bits organized in various ways, trying to determine the status of a spacecraft. Understanding the data often required additional levels of GDS expertise, or worse, transformation of the raw data into alternative formats followed by ingestion into other tools so that the data became meaningful. The primary focus was often to answer these types of questions: "Why did this particular frame fail Reed-Solomon decode? Why did this packet get marked as invalid? Why am I missing a block of telemetry from my query?" -- which are completely valid questions to ask from a GDS Engineer's point of view, and large families of tools have been designed to help answer these questions. But these are not the questions that most users care about - which are more like: "Why is the battery state of charge trending down? Show me a summary image report for the last traverse to the target. Show me a data accountability summary for the last DSN pass." Answers to these questions, which are what users are looking for, requires a higher level of abstraction and supporting tools than mining through ones and zeros. JPL has created a next generation capability called the Mission Data Processing and Control System (MPCS) which is designed to support this higher level of abstraction by providing customizable views of the ground system combining collections of lower level information into more meaningful ways. Instead of examining frames, packets, and individual telemetry data points -- MPCS is capable of providing comprehensive summary reports, product status, overall flight/ground event status, as well as payload health summaries. Based on these higher level views, end users can make tactical or strategic decisions, or drop into detailed analysis as needed. System designers need to continue building systems that support low level GDS troubleshooting - but the basic design of a GDS should be geared towards what end users actually need to see. This paper will describe the capabilities of MPCS that directly support these higher levels of abstraction, and which are being used today in missions such as the Mars Science Laboratory and other NASA missions.
As NASA prepares for crewed lunar missions over the next several years, plans are also underway to journey farther into deep space. Deep space exploration will require a paradigm shift in astronaut medical support toward progressively earth-independent medical operations (EIMO). The Exploration Medical Capability (ExMC) element of NASA’s Human Research Program (HRP) is investigating the feasibility and value of advanced capabilities to promote and enhance EIMO. Currently, astronauts rely on real-time communication with ground-based medical providers. However, as the distance from Earth increases, so do communication delays and disruptions. Moreover, resupply and evacuation will become increasingly complex, if not impossible, on deep space missions. In contrast to today’s missions in low earth orbit (LEO), where most medical expertise and decision-making are ground-based, an exploration crew will need to autonomously detect, diagnose, treat, and prevent medical events. Due to the sheer amount of pre-mission training required to execute a human spaceflight mission, there is often little time to devote exclusively to medical training. One potential solution is to augment the long duration exploration crew’s knowledge, skills, and abilities with a clinical decision support system (CDSS). An analysis of preliminary data indicates the potential benefits of a CDSS to mission outcomes when augmenting cognitive and procedural performance of an autonomous crew performing medical operations, and we provide an illustrative scenario of how such a CDSS might function.
Researchers at the U.S. Department of Energy's Argonne National Laboratory and the University of Chicago are developing tools that merge urban design with scientific analysis to improve the decision-making process associated with large-scale urban developments. One such tool, called LakeSim, has been prototyped with an initial focus on consumer-driven energy and transportation demand, through a partnership with the Chicago-based architectural and engineering design firm Skidmore, Owings & Merrill, Clean Energy Trust and developer McCaffery Interests. LakeSim began with the need to answer practical questions about urban design and planning, requiring a better understanding about the long-term impact of design decisions on energy and transportation demand for a 600-acre development project on Chicago's South Side - the Chicago Lakeside Development project.
Target detection is one fundamental problem in many sensor network-based applications, which is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as 2C-SSEM,which combines 2-level clustering-based sensor deployment and source strength estimate map-based data fusion for the detection of a single static or moving target. 2C-SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into several subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, 2C-SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.
In 2019, U.S. Department of Energy (DOE) contractor CH2M Hill Plateau Remediation Company (CHPRC) completed structurally stabilizing the Plutonium Uranium Extraction Plant (Purex) waste storage Tunnel 2 with engineered grout. This action significantly reduced risk to Hanford workers, the surrounding community and environment by preventing future collapse of the tunnel and possible release of radioactive materials. Decades of plutonium production at the Purex facility required construction of two large underground tunnels for storage of highly contaminated plutonium processing equipment that had failed during the operation of Purex and other facilities across the Hanford Site. Following the partial collapse of Purex Tunnel 1 on May 9, 2017, DOE and CHPRC took immediate action to safely stabilize the collapsed section of the tunnel. The hole in the tunnel was filled the next day as a temporary stabilization measure, and no contamination was detected as a result of the partial collapse of the tunnel. Workers then stabilized Tunnel 1 in its entirety by filling with engineered grout, which also encapsulated the radioactive equipment and railcars within the tunnel. Grouting eliminated the risk of further collapse and stabilized the tunnel contents, all while not precluding future remedial actions or final closure decisions. While conducting an integrity analysis of Tunnel 1 by using construction drawings, photos and other historical documents, project teams also analyzed the much larger Purex Tunnel 2. The analysis found Tunnel 2 did not meet current codes for structural integrity, and it may not be able to bear the weight of the soil above the tunnel. Therefore, it was determined that Tunnel 2 was also at risk of collapse. DoE's independent panel of experts determined grouting to be the best choice for stabilizing Tunnel 2 because it provides the highest level of stability and protection, and does not preclude future remedial actions. DOE authorized CHPRC to commence work and the Purex Tunnel 2 Structural Stabilization Project was launched in December of 2017. The paper will discuss the process of recognizing the following critical aspects and risks associated with stabilizing Tunnel 2, and the actions taken by the project team to mitigate these risks, including: - Tunnel investigation and data collection to assist with future remediation; - On-site batching and mock-ups to provide the large quantity of grout required for stabilizing the tunnel; - Workforce involvement and innovative actions that mitigated the project's most significant potential hazards, including traffic control; - Project team high level of execution translated into successful project execution. Sharing lessons learned from this critical risk-reduction effort can enhance the safety and efficiency of other complex and hazardous projects across the DOE complex. (authors)
Renewable energy auctions are now a common competitive approach to procure low-cost renewable power around the world. Ensuring a successful auction process increasingly depends on the capabilities of auction designers and participants to identify actionable and defensible insights from large data sets (on renewable energy resources and complementary data) to both attract potential investors and address stakeholder concerns. The Renewable Energy (RE) Data Explorer is a user-friendly geospatial analysis tool for analyzing renewable energy potential and informing decisions. Developed by the National Renewable Energy Laboratory (NREL) and supported by the U.S. Agency for International Development (USAID), RE Data Explorer performs visualization and analysis of renewable energy potential that can be customized for different scenarios. RE Data Explorer can support prospecting, integrated planning, policymaking, and other decision-making activities to accelerate renewable energy deployment. The broader RE Explorer website provides guidance and information to link the RE Data Explorer geospatial analysis tool to key decision areas. This document provides information on how the RE Data Explorer can be used to support renewable energy auction processes.
The increasing deployment of distribution-connected photovoltaic (DPV) systems requires utilities to complete ever more complex interconnection studies. Relatively simple interconnection study methods worked well for low penetrations of photovoltaic systems, but more complicated quasistatic time-series (QSTS) analysis is required to make better interconnection decisions as DPV penetration levels increase. Tools and methods must be developed to support this type of complex analysis to enable efficient completion of PV impact studies. This paper presents a variable-time-step solver for QSTS analysis that significantly shortens the computational time and effort to complete a detailed analysis of the operation of a distribution circuit with many DPV systems. Specifically, it demonstrates that the proposed variable-time-step solver can reduce the required computational time by as much as 84% without introducing any important errors to metrics, such as the number of voltage regulator tap operations and highest and lowest voltage occurring on the distribution circuit during a 1-yr simulation period. Further improvement in computational speed is possible with the introduction of only modest errors in these metrics, such as a 91% reduction with less than 5% error when predicting voltage regulator operations. Furthermore, an alternate variable time-step method is also shown to perform adequately and shows promise of even greater reductions in simulation runtime.
Energy-water nexus has substantially increased importance in the recent years. Synergistic approaches based on systems-analysis and mathematical models are critical for helping decision makers better understand the interrelationships and tradeoffs between energy and water. In energywater nexus management, various decision makers with different goals and preferences, which are often conflicting, are involved. These decision makers may have different controlling power over the management objectives and the decisions. They make decisions sequentially from the upper level to the lower level, challenging decision making in energy-water nexus. In order to address such planning issues, a bi-level decision model is developed, which improves upon the existing studies by integration of bi-level programming into energy-water nexus management. The developed model represents a methodological contribution to the challenge of sequential decisionmaking in energy-water nexus through provision of an integrated modeling framework/tool. An interactive fuzzy optimization methodology is introduced to seek a satisfactory solution to meet the overall satisfaction of the two-level decision makers. The tradeoffs between the two-level decision makers in energy-water nexus management are effectively addressed and quantified. Application of the proposed model to a synthetic example problem has demonstrated its applicability in practical energy-water nexus management. Optimal solutions for electricity generation, fuel supply, water supply including groundwater, surface water and recycled water, capacity expansion of the power plants, and GHG emission control are generated. In conclusion, these analyses are capable of helping decision makers or stakeholders adjust their tolerances to make informed decisions to achieve the overall satisfaction of energy-water nexus management where bi-level sequential decision making process is involved.
A preliminary target activation analysis for the Second Target Station (STS) at the Spallation Neutron Source (SNS) located at Oak Ridge National Laboratory (ORNL) was preformed using Monte Carlo N-Particle X (MCNPX), CINDER90, and the corresponding multi-group cross section libraries distributed with CINDER90. This preliminary analysis has been used to guide some design decisions of the STS target design. Using the MCNPX output files from this preliminary analysis, a comparison of the activation analysis using CINDER90 and CINDER2008 was performed. While the CINDER90 code has been validated for use in spallation source activation and transmutation calculations, and compares well with measurements, the transition to the modernized version of the code (CINDER2008) and the updated cross section libraries is desirable. The goal of this comparison is to evaluate the differences and implications for the STS activation analysis. Overall, the total activity differs between CINDER2008 and CINDER90 by no more than 20% for the materials evaluated in this report. The largest discrepancies were observed in the decay gamma intensity densities with CINDER90 calculating a factor of 2 higher than CINDER2008.
The development of a comprehensive analytical model of rotorcraft aerodynamics and dynamics is described. Particular emphasis is given to describing the reasons behind the choices and decisions involved in constructing the model. The analysis is designed to calculate rotor performance, loads and noise; helicopter vibration and gust response; flight dynamics and handling qualities; and system aeroelastic stability. It is intended for use in the design, testing and evaluation of a wide class of rotors and rotorcraft, and to be the basis for further development of rotary wing theories. The general characteristics of the geometric, structural, inertial, and aerodynamic models used for the rotorcraft components are described, including the assumptions introduced by the chosen models and the resulting capabilities and limitations. Finally, some examples from recent applications of the analysis are given.
The Decision Support Tool helps users address four discrete challenges: (1) complex requirements of relevant codes and standards, (2) technical complexity of solar assets, (3) undefined cyber risk severity, and (4) unclear roles and responsibilities. Content includes an analysis of cyber vulnerability risks, a decision support resource for policymakers to mitigate cyber risks to solar photovoltaic systems, and background resources for informing policy development. A key component of this tool is the Probable Risk Assessment (PRA), based on established, formal variables, models, and consequences, which helps users understand the determined risk and assigned ownership of the physical components. It helps states draw logical lines between vulnerabilities and mitigative solutions, of which some have been further detailed as Cybersecurity Advisory Team for State Solar (CATSS) tools.