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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Seismic Resilience of Large Power Transformer Bushings & Non-SF6 Industrial Base Scan Review

Large (high voltage) power transformers (LPT), and more specifically, their bushings, are known to be susceptible to seismic failure. With bushing failure, a transformer will have to be replaced, which has a considerable lead time, adding to the power outage duration. Cost-efficient, proven solutions are not currently available to mitigate this risk, which can persist for the more than 30-year life of a particular transformer. This work will focus on developing and demonstrating a hardware solution to address seismic vulnerabilities and reduce outage risks from LPT failure. Sulfur Hexafluoride (SF6) is a specialty gas with excellent electrical insulation properties which has been used extensively in the power industry. This gas is unfortunately also one of the most potent greenhouse gases known to humanity. A 2014 report by the Intergovernmental Panel on Climate Change found that SF6 has a global warming potential (GWP) 23,000 times higher than Carbon Dioxide, and has the highest GWP of all gases assessed (Myhre 2013). SF6 is almost exclusively man-made and is produced for use as an insulator in high voltage electrical equipment. This makes the production and use of SF6 one of the leading sources of anthropogenic climate change. To fully eliminate the environmental impacts of SF6, alternative technology is needed. The ideal replacement would be a technology that can fulfil the same role as SF6, at the same cost or cheaper, but without adverse environmental effects. Currently, no technology fits this description, however several promising technologies have begun to enter the market. An industry scan was performed to assess the state of industry adoption and manufacturing capability for SF6-free alternative technologies for use at the high-voltage level, and the primary barriers to broader adoption.

10 SYNTHETIC FUELS↗
NASA NTRS1997

Fatigue Reliability of Gas Turbine Engine Structures

The results of an investigation are described for fatigue reliability in engine structures. The description consists of two parts. Part 1 is for method development. Part 2 is a specific case study. In Part 1, the essential concepts and practical approaches to damage tolerance design in the gas turbine industry are summarized. These have evolved over the years in response to flight safety certification requirements. The effect of Non-Destructive Evaluation (NDE) methods on these methods is also reviewed. Assessment methods based on probabilistic fracture mechanics, with regard to both crack initiation and crack growth, are outlined. Limit state modeling techniques from structural reliability theory are shown to be appropriate for application to this problem, for both individual failure mode and system-level assessment. In Part 2, the results of a case study for the high pressure turbine of a turboprop engine are described. The response surface approach is used to construct a fatigue performance function. This performance function is used with the First Order Reliability Method (FORM) to determine the probability of failure and the sensitivity of the fatigue life to the engine parameters for the first stage disk rim of the two stage turbine. A hybrid combination of regression and Monte Carlo simulation is to use incorporate time dependent random variables. System reliability is used to determine the system probability of failure, and the sensitivity of the system fatigue life to the engine parameters of the high pressure turbine. 'ne variation in the primary hot gas and secondary cooling air, the uncertainty of the complex mission loading, and the scatter in the material data are considered.

Cruse, Thomas A.↗
NASA NTRS1992

Real-time diagnostics for a reusable rocket engine

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Guo, T. H.↗
NASA NTRS2007

Modeling the Prodromal Effects and Performance Reduction of Astronauts from Exposure to Large Solar Particle Events

In space exploration outside the Earth's geomagnetic field, radiation exposure from solar particle events (SPE) presents a health concern for astronauts, that could impair their performance and result in possibility of failure of the mission. Acute risks are especially of concern during spacewalks on the lunar surface because of the rapid onset of SPE's and science goals that involve long distances to crew habitats. Thus assessing the potential of early radiation effect under such adverse conditions is of prime importance. Here we present a biologic based mathematical model which describes the dose and time-dependent early human responses to ionizing radiation. We examine the possible early effects on crew behind various shielding materials from exposure to some historical large SPEs on the lunar and Mars surfaces. The doses and dose rates were calculated using the BRYNTRN code (Kim, M.Y, Hu, X, and Cucinotta, F.A, Effect of Shielding Materials from SPEs on the Lunar and Mars Surface, AIAA Space 2005, paper number AIAA-2005-6653, Long Beach, CA, August 30-September 1, 2005) and the hazard of the early radiation effects and performance reduction were calculated using the RIPD code (Anno, G.H, McClellan, G.E., Dore, M.A, Protracted Radiation-Induced Performance Decrement, Volume 1 Model Development,1996, Defense Nuclear Agency: Alexandria VA). Based on model assumptions we show that exposure to these historical SPEs do cause early effects to crew members and impair their performance if effective shielding and medical countermeasure tactics are not provided. The calculations show multiple occurrence of large SPEs in a short period of time significantly increase the severity of early illness, however early death from failure of the hematopoietic system is very unlikely because of the dose-rate and dose heterogeneity of SPEs. Results from these types of calculations will be a guide in design of protection systems and medical response strategy for astronauts in case of exposure to high dose irradiation during future space missions.

Hu, S.↗
NASA NTRS1996

Conversion-Integration of MSFC Nonlinear Signal Diagnostic Analysis Algorithms for Realtime Execution of MSFC's MPP Prototype System

NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.

Jong, Jen-Yi↗
DOE OSTI2024

Slow dynamic elasticity at short times

It has been reported that slow dynamic nonlinear elastic relaxations, widely thought to proceed universally in proportion to the logarithm of time after cessation of mechanical conditioning, actually recover with a smaller slope at early times, with a time of transition that varies with the grain size of the material. Furthermore, this would constitute a heretofore unreported failure of the claimed universality, while suggesting application to nondestructive evaluation and structural health monitoring. Here we present new observations at short times, in the single bead system, in cement paste, mortar, concrete, sandstone and granite. Within the limits imposed by finite duration ring-down such that the effective instant of conditioning cessation is imprecise, and the corresponding ambiguity as to the time at which relaxation begins, we find no reliable sign of such a transition, even in samples of large grain size mortar and concrete similar to those described elsewhere as having clear and late cutoffs.

58 GEOSCIENCES↗
DOE OSTI2020

Reliable machine prognostic health management in the presence of missing data

Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in-service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time-series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data-driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one-hot health state indicators are appended to the historical time series so that the model learns end-of-life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Furthermore, experiments on multivariate time-series datasets collected from real-world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long-term prognostics.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Scaling up carboxylic acid production from cheese whey and brewery wastewater via methane-arrested anaerobic digestion

In a circular economy, organic waste streams are valuable resources for sustainably producing chemicals and fuels. This study investigates a new methane-arrested anaerobic digestion (MAAD) process that converts high-strength cheese whey and brewery wastewater into carboxylic acids. The process was developed and optimized under various bench-scale semi-continuous (fed-batch) operating conditions (e.g., retention time, organic loading rate, pH, and feed/harvest frequency). The MAAD responses to various control-failure scenarios were also systematically investigated. The highest total acid productivity was 26g/(L liq ·d) with a substrate conversion of 0.79g COD digested /g COD fed at a hydraulic retention time (HRT) of approximately 2 d in a 14-L digester. The most stable conditions for digester operation (HRT 3 d at pH 6.0 and 40°C) were selected for process scale-up to 100gal (~380 L). Semi-continuous, pilot-scale MAAD successfully produced a total acid concentration of 40.6+/-1.1 g/L with 8.1% acetic acid, 45.1% butyric acid, and 44.5% lactic acid. The links between wastewater characteristics, operation mode, digester scale, and microbial community structure were statistically analyzed. The results show genera Sporolactobacillus and Clostridium positively correlate with butyric acid production (Pearson correlation coefficient>0.5). Moreover, four kinetic models were developed and fit to batch MAAD experimental datasets (R 2 >95%) and were successfully applied to predict the total acid production in both bench-and pilot-scale semi-continuous MAADs. In conclusion, this study shows MAAD has the potential for industrial-scale applications and is a robust platform to valorize low- or negative-value waste streams into high-value bioproducts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

Real-Time Adaptive Control Allocation Applied to a High Performance Aircraft

Abstract This paper presents the development and application of one approach to the control of aircraft with large numbers of control effectors. This approach, referred to as real-time adaptive control allocation, combines a nonlinear method for control allocation with actuator failure detection and isolation. The control allocator maps moment (or angular acceleration) commands into physical control effector commands as functions of individual control effectiveness and availability. The actuator failure detection and isolation algorithm is a model-based approach that uses models of the actuators to predict actuator behavior and an adaptive decision threshold to achieve acceptable false alarm/missed detection rates. This integrated approach provides control reconfiguration when an aircraft is subjected to actuator failure, thereby improving maneuverability and survivability of the degraded aircraft. This method is demonstrated on a next generation military aircraft Lockheed-Martin Innovative Control Effector) simulation that has been modified to include a novel nonlinear fluid flow control control effector based on passive porosity. Desktop and real-time piloted simulation results demonstrate the performance of this integrated adaptive control allocation approach.

Davidson, John B.↗
NASA NTRS2012

Influence of Finite Element Size in Residual Strength Prediction of Composite Structures

The sensitivity of failure load to the element size used in a progressive failure analysis (PFA) of carbon composite center notched laminates is evaluated. The sensitivity study employs a PFA methodology previously developed by the authors consisting of Hashin-Rotem intra-laminar fiber and matrix failure criteria and a complete stress degradation scheme for damage simulation. The approach is implemented with a user defined subroutine in the ABAQUS/Explicit finite element package. The effect of element size near the notch tips on residual strength predictions was assessed for a brittle failure mode with a parametric study that included three laminates of varying material system, thickness and stacking sequence. The study resulted in the selection of an element size of 0.09 in. X 0.09 in., which was later used for predicting crack paths and failure loads in sandwich panels and monolithic laminated panels. Comparison of predicted crack paths and failure loads for these panels agreed well with experimental observations. Additionally, the element size vs. normalized failure load relationship, determined in the parametric study, was used to evaluate strength-scaling factors for three different element sizes. The failure loads predicted with all three element sizes provided converged failure loads with respect to that corresponding with the 0.09 in. X 0.09 in. element size. Though preliminary in nature, the strength-scaling concept has the potential to greatly reduce the computational time required for PFA and can enable the analysis of large scale structural components where failure is dominated by fiber failure in tension.

Satyanarayana, Arunkumar↗
NASA NTRS2006

Advanced Technologies for Future Spacecraft Cockpits and Space-based Control Centers

The National Aeronautics and Space Administration (NASA) is embarking on a new era of Space Exploration, aimed at sending crewed spacecraft beyond Low Earth Orbit (LEO), in medium and long duration missions to the Lunar surface, Mars and beyond. The challenges of such missions are significant and will require new technologies and paradigms in vehicle design and mission operations. Current roles and responsibilities of spacecraft systems, crew and the flight control team, for example, may not be sustainable when real-time support is not assured due to distance-induced communication lags, radio blackouts, equipment failures, or other unexpected factors. Therefore, technologies and applications that enable greater Systems and Mission Management capabilities on-board the space-based system will be necessary to reduce the dependency on real-time critical Earth-based support. The focus of this paper is in such technologies that will be required to bring advance Systems and Mission Management capabilities to space-based environments where the crew will be required to manage both the systems performance and mission execution without dependence on the ground. We refer to this concept as autonomy. Environments that require high levels of autonomy include the cockpits of future spacecraft such as the Mars Exploration Vehicle, and space-based control centers such as a Lunar Base Command and Control Center. Furthermore, this paper will evaluate the requirements, available technology, and roadmap to enable full operational implementation of onboard System Health Management, Mission Planning/re-planning, Autonomous Task/Command Execution, and Human Computer Interface applications. The technology topics covered by the paper include enabling technology to perform Intelligent Caution and Warning, where the systems provides directly actionable data for human understanding and response to failures, task automation applications that automate nominal and Off-nominal task execution based on human input or integrated health state-derived conditions. Shifting from Systems to Mission Management functions, we discuss the role of automated planning applications (tactical planning) on-board, which receive data from the other cockpit automation systems and evaluate the mission plan against the dynamic systems and mission states and events, to provide the crew with capabilities that enable them to understand, change, and manage the timeline of their mission. Lastly, we discuss the role of advanced human interface technologies that organize and provide the system md mission information to the crew in ways that maximize their situational awareness and ability to provide oversight and control of aLl the automated data and functions.

Garcia-Galan, Carlos↗
NASA NTRS2013

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

System & Software Health Management↗
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↗
NASA NTRS2012

Oxidation and the Effects of High Temperature Exposures on Notched Fatigue Life of an Advanced Powder Metallurgy Disk Superalloy

Oxidation and the effects of high temperature exposures on notched fatigue life were considered for a powder metallurgy processed supersolvus heat-treated ME3 disk superalloy. The isothermal static oxidation response at 704 C, 760 C, and 815 C was consistent with other chromia forming nickel-based superalloys: a TiO2-Cr2O3 external oxide formed with a branched Al2O3 internal subscale that extended into a recrystallized - dissolution layer. These surface changes can potentially impact disk durability, making layer growth rates important. Growth of the external scales and dissolution layers followed a cubic rate law, while Al2O3 subscales followed a parabolic rate law. Cr- rich M23C6 carbides at the grain boundaries dissolved to help sustain Cr2O3 growth to depths about 12 times thicker than the scale. The effect of prior exposures was examined through notched low cycle fatigue tests performed to failure in air at 704 C. Prior exposures led to pronounced debits of up to 99 % in fatigue life, where fatigue life decreased inversely with exposure time. Exposures that produced roughly equivalent 1 m thick external scales at the various isotherms showed statistically equivalent fatigue lives, establishing that surface damage drives fatigue debit, not exposure temperature. Fractographic evaluation indicated the failure mode for the pre-exposed specimens involved surface crack initiations that shifted with exposure from predominately single intergranular initiations with transgranular propagation to multi-initiations from the cracked external oxide with intergranular propagation. Weakened grain boundaries at the surface resulting from the M23C6 carbide dissolution are partially responsible for the intergranular cracking. Removing the scale and subscale while leaving a layer where M23C6 carbides were dissolved did not lead to a significant fatigue life improvement, however, also removing the M23C6 carbide dissolution layer led to nearly full recovery of life, with a transgranular initiation typical to that observed in unexposed specimens.

Sudbrack, Chantal K.↗
DOE OSTI2025

Ultrasonic Characterization of Lithium Ion Thermal Runaway Conditions for Real-time Ultrasonic Enabled BMS Integration

The demand for energy storage is growing, and lithium-ion batteries are a promising technology to meet this need due to their high power/energy density, high round-trip efficiency, rapid response time, and portability. However, recent catastrophic events caused by thermal runaway have slowed their adoption, highlighting the need for an early warning system for battery failure. In this work, ultrasound is used to detect physical changes in 950 mAh batteries by identifying material property changes independent of voltage and current. [Ultrasound signal features (e.g., time of flight, maximum frequency component) were extracted as the batteries were cycled and subjected to both constant current and constant voltage overcharge and were used to develop two metrics identifying failure: a warning to detect the start of overcharge and an emergency stop (E-stop) to immediately take the battery out of service. The identification method involved locating magnitude differences of several ultrasound features compared to baseline operation considering different currents and temperatures, and the warning/stopping metrics were consistent across all experiments. For an average overcharge time of 140 minutes, the average warning was issued 124 minutes before the failure and the average E-stop was triggered 94 minutes before failure. As a test of using ultrasound for early warning detection, a battery was forced into overcharge and returned to normal cycling conditions based on the previously determined warning metric. Both the voltage profile and the ultrasound measurements returned to their baseline behavior, indicating that ultrasonic detection can not only identify battery failure before a catastrophic event, but can also provide early enough warnings such that overcharges can be detected and corrected quickly enough so the battery does not need to be decommissioned.]

25 ENERGY STORAGE↗
DOE OSTI2023

Reliability of Open Public Electric Vehicle Direct Current Fast Chargers

The aim was to systematically evaluate the usability of all public electric vehicles (EV) direct current fast chargers (DCFC) in the San Francisco region. To achieve a rapid transition to EVs, a highly reliable and easy to use charging infrastructure is critical to building confidence among consumers. The functionality and usability of all 182 open, public DCFC charging stations with CCS connectors (combined charging system) in the 9 counties of the Bay Area were tested (655 electric vehicle service equipment (EVSE) ports). An EVSE was classified as functional if it charged an EV for 2 minutes. Overall, 73.3% of the 655 EVSEs were functional. The causes of the nonfunctioning EVSEs (23.5%) were blank or unresponsive screens or error messages; payment system failures; charge initiation failures; network failures; or broken connectors. In addition, the cable was too short to reach the EV inlet for 3.2% of the EVSEs. A random sampling of 10% of the EVSEs, approximately 8 days after the first evaluation, found no overall change in functionality. The level of functionality found with field testing conflicts with the 95–98% uptime reported by the EV service providers (EVSPs) who operate the EV charging stations. There is a need for precise and verifiable definitions of uptime, downtime, and excluded time, as applied to public EV chargers. In conclusion, the level of failure of the existing public EV DCFC charge infrastructure highlights the importance of improving the system design and maintenance to improve adoption of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1997

Experimental Investigation of a Graphite-Composite Intertank Section for a Reusable Launch Vehicle

The use of graphite-composites as primary structure is essential for the development of a cost effective single-stage-to-orbit reusable launch vehicle (RLV). A full-scale segment of a graphite-composite intertank was designed and fabricated by an industry team and tested at NASA Langley Research Center as a part of the RLV technology development program. The intertank test component was approximately 3.0 m long by 6.7 m wide and included slightly less than a 90deg section of a cylindrical intertank. The test article was fabricated using graphite-bismaelimide composite material and consisted of a hat-stiffened skin with five ring frames. The stiffeners were secondarily bonded to the panel skin and the ring frames were mechanically attached. The test article was loaded to failure with a uniform compression load applied by 21 individually controlled hydraulic actuators. Resistance strain gages, linear variable differential transformers and video cameras were used to measure and record the test results. A linear f d t e element analysis of the test article and test fixture was performed. The test results verified the structural integrity cf the intertank test article up to the design limit load. However, the test article failed in general skin buckling between the ring frames at a load 1.08 times design limit load. This load was considerably lower than the expected failure load. The cause of premature failure is being investigated. Good correlation of the test and analysis results was obtained for loads up to the design limit load.

Sawyer, James Wayne↗
NASA NTRS2000

ACIS Door Failure Investigation and Mitigation Procedures

NASA's Chandra X-ray Observatory (formerly AXAF) was launched on July 23, 1999 and is currently in orbit performing scientific studies. Chandra is the third of NASA's Great Observatories to be launched, following the Hubble Space Telescope and the Compton Gamma Ray Observatory. One of four primary science instruments on Chandra, and one of only two focal plane instruments, is the Advanced CCD Imaging Spectrometer, or ACIS. The ACIS focal plane and Optical Blocking Filter (OBF) must be launched under vacuum, so a tightly sealed, functioning door and venting subsystem were implemented. The door was opened two and one-half weeks after launch (after most out-gassing of composite materials) and allowed X-rays to be imaged by the ACIS CCD's in the focal plane. A failure of this door to open on-orbit would have eliminated all ACIS capabilities, severely degrading mission science. During the final pre-flight thermal-vacuum test of the fully integrated Chandra Observatory at TRW, the ACIS door failed to open when commanded to do so. This paper describes the efforts, under considerable time pressure, by NASA, its contractors and outside review teams to investigate the failure and to develop modified hardware and procedures which would correct the problem. Of interest is the fact that the root cause of the test failure was never clearly identified despite massive effort. We ultimately focussed on hardware and procedures designed to mitigate the effects of potential, but unproven, failure modes. We describe a frequent real-world engineering situation in which one must proceed on the best basis possible in the absence of the complete set of facts.

Podgorski, William A.↗