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The use of Electric Auxiliary Power Unit (EAPU) in commercial, and military aviation, and in commercial electric automobiles, is reviewed. The specifications of the battery to be used in the space shuttle are reviewed, and the possible vendors discussed. The testing activity and the reliability and the safety features are presented.
Data assimilation is all about understanding the error characteristics of the data and models that are used in the assimilation process. Reliable error estimates are needed to implement observational quality control, bias correction of observations and model fields, and intelligent data selection. Meaningful covariance specifications are obviously required for the analysis as well, since the impact of any single observation strongly depends on the assumed structure of the background errors. Operational atmospheric data assimilation systems still rely primarily on climatological background error covariances. To obtain error estimates that reflect both the character of the flow and the current state of the observing system, it is necessary to solve three problems: (1) how to account for the short-term evolution of errors in the initial conditions; (2) how to estimate the additional component of error caused by model defects; and (3) how to compute the error reduction in the analysis due to observational information. Various approaches are now available that provide approximate solutions to the first and third of these problems. However, the useful accuracy of these solutions very much depends on the size and character of the model errors and the ability to account for them. Model errors represent the real-world forcing of the error evolution in a data assimilation system. Clearly, meaningful model error estimates and/or statistics must be based on information external to the model itself. The most obvious information source is observational, and since the volume of available geophysical data is growing rapidly, there is some hope that a purely statistical approach to model error estimation can be viable. This requires that the observation errors themselves are well understood and quantifiable. We will discuss some of these challenges and present a new sequential scheme for estimating model error variances from observations in the context of an atmospheric data assimilation system.
Ceratopteris richardii is an aquatic fern grown in tropical and subtropical regions of the world. It is proven to be a productive model system for studies in the genetics, biochemistry, and cell biology of basic biologic processes that occur in early gametophytic development. It provides several advantages to biologists, especially those interested in gravitational biology, polarity development, and in the genetics of sexual development. It is easy to culture, has a relatively short life cycle, and offers an array of attractive features that facilitate genetic studies. The germination and early development of large populations of genetically identical spores are easy to synchronize, and both the direction of polarity development and cell-level gravity responses can be measured and readily manipulated within the first 24 h of spore development. Although there is no reliable transformation system available yet in Ceratopteris, recent studies suggest that the technique of RNA interference can be used to block translation of specific genes in a related fern, Marsilea, and current studies will soon reveal the applicability of this approach, as well as of other transformation approaches, in Ceratopteris. A recently completed expressed sequence tag (EST) sequencing project makes available the partial sequence of more than 2000 cDNAs, representing a significant percentage of the genes being expressed during the first 24 h of spore germination, when many developmentally interesting processes are occurring. A microarray of these ESTs is being constructed, so especially for those scientists interested in basic cellular phenomena that occur early in spore germination, the availability of the ESTs and of the microarray will make Ceratopteris an even more attractive model system.
The use of computer hardware and software in life-critical applications, such as for civil air transports, demands the use of rigorous formal mathematical validation procedures. However, formal specification and verification will only be tractable if the system is designed in a manner that lends itself to formal methods. Likewise, accurate reliability analysis will only be tractable if the number of interacting components that must be individually included in a single reliability model is kept to a low number and if their failure behavior interactions can be modeled simply. Also, the system must be designed such that the system reliability does not directly depend on system parameters that cannot be accurately determined. This paper presents a design methodology based on the concept of designing a system in such a manner that it can be rigorously validated, or "design for validation."
In principle, formal methods offer many advantages for aerospace software development: they can help to achieve ultra-high reliability, and they can be used to provide evidence of the reliability claims which can then be subjected to external scrutiny. However, despite years of research and many advances in the underlying formalisms of specification, semantics, and logic, formal methods are not much used in practice. In our opinion this is related to three major shortcomings. First, the application of formal methods is still expensive because they are labor- and knowledge-intensive. Second, they are difficult to scale up to complex systems because they are based on deep mathematical insights about the behavior of the systems (t.e., they rely on the "heroic proof"). Third, the proofs can be difficult to interpret, and typically stand in isolation from the original code. In this paper, we describe a tool for formally demonstrating safety-relevant aspects of aerospace software, which largely circumvents these problems. We focus on safely properties because it has been observed that safety violations such as out-of-bounds memory accesses or use of uninitialized variables constitute the majority of the errors found in the aerospace domain. In our approach, safety means that the program will not violate a set of rules that can range for the simple memory access rules to high-level flight rules. These different safety properties are formalized as different safety policies in Hoare logic, which are then used by a verification condition generator along with the code and logical annotations in order to derive formal safety conditions; these are then proven using an automated theorem prover. Our certification system is currently integrated into a model-based code generation toolset that generates the annotations together with the code. However, this automated formal certification technology is not exclusively constrained to our code generator and could, in principle, also be integrated with other code generators such as RealTime Workshop or even applied to legacy code. Our approach circumvents the historical problems with formal methods by increasing the degree of automation on all levels. The restriction to safety policies (as opposed to arbitrary functional behavior) results in simpler proof problems that can generally be solved by fully automatic theorem proves. An automated linking mechanism between the safety conditions and the code provides some of the traceability mandated by process standards such as DO-178B. An automated explanation mechanism uses semantic markup added by the verification condition generator to produce natural-language explanations of the safety conditions and thus supports their interpretation in relation to the code. It shows an automatically generated certification browser that lets users inspect the (generated) code along with the safety conditions (including textual explanations), and uses hyperlinks to automate tracing between the two levels. Here, the explanations reflect the logical structure of the safety obligation but the mechanism can in principle be customized using different sets of domain concepts. The interface also provides some limited control over the certification process itself. Our long-term goal is a seamless integration of certification, code generation, and manual coding that results in a "certified pipeline" in which specifications are automatically transformed into executable code, together with the supporting artifacts necessary for achieving and demonstrating the high level of assurance needed in the aerospace domain.
The United States National Aeronautics and Space Administration (NASA) is in the midst of a space exploration program intended for sending crew and cargo to the international Space Station (ISS), to the moon, and beyond. This program is called Constellation. As part of the Constellation program, NASA is developing new launch vehicles aimed at significantly increase safety and reliability, reduce the cost of accessing space, and provide a growth path for manned space exploration. Achieving these goals requires a rigorous process that addresses reliability, safety, and cost upfront and throughout all the phases of the life cycle of the program. This paper discusses the "Design for Reliability and Safety" approach for the NASA new crew launch vehicle called ARES I. The ARES I is being developed by NASA Marshall Space Flight Center (MSFC) in support of the Constellation program. The ARES I consists of three major Elements: A solid First Stage (FS), an Upper Stage (US), and liquid Upper Stage Engine (USE). Stacked on top of the ARES I is the Crew exploration vehicle (CEV). The CEV consists of a Launch Abort System (LAS), Crew Module (CM), Service Module (SM), and a Spacecraft Adapter (SA). The CEV development is being led by NASA Johnson Space Center (JSC). Designing for high reliability and safety require a good integrated working environment and a sound technical design approach. The "Design for Reliability and Safety" approach addressed in this paper discusses both the environment and the technical process put in place to support the ARES I design. To address the integrated working environment, the ARES I project office has established a risk based design group called "Operability Design and Analysis" (OD&A) group. This group is an integrated group intended to bring together the engineering, design, and safety organizations together to optimize the system design for safety, reliability, and cost. On the technical side, the ARES I project has, through the OD&A environment, implemented a probabilistic approach to analyze and evaluate design uncertainties and understand their impact on safety, reliability, and cost. This paper focuses on the use of the various probabilistic approaches that have been pursued by the ARES I project. Specifically, the paper discusses an integrated functional probabilistic analysis approach that addresses upffont some key areas to support the ARES I Design Analysis Cycle (DAC) pre Preliminary Design (PD) Phase. This functional approach is a probabilistic physics based approach that combines failure probabilities with system dynamics and engineering failure impact models to identify key system risk drivers and potential system design requirements. The paper also discusses other probabilistic risk assessment approaches planned by the ARES I project to support the PD phase and beyond.
The National Aeronautics and Space Administration (NASA) has identified liquid oxygen (LO2)/liquid methane (LCH4) propulsion systems as promising options for some future space vehicles. NASA issued a contract to Aerojet to develop a 100-lbf (445 N) LO2/LCH4 Reaction Control Engine (RCE) aimed at reducing the risk of utilizing a cryogenic reaction control system (RCS) on a space vehicle. Aerojet utilized innovative design solutions to develop an RCE that can ignite reliably over a broad range of inlet temperatures, perform short minimum impulse bits (MIB) at small electrical pulse widths (EPW), and produce excellent specific impulse (Isp) across a range of engine mixture ratios (MR). These design innovations also provide a start transient with a benign MR, ensuring good thrust chamber compatibility and long life. In addition, this RCE can successfully operate at MRs associated with main engines, enabling the RCE to provide emergency backup propulsion to minimize vehicle propellant load and overall system mass.
The National Aeronautics and Space Administration (NASA) has identified liquid oxygen (LO2)/liquid methane (LCH4) propulsion systems as promising options for some future space vehicles. NASA issued a contract to Aerojet to develop a 100-lbf (445 N) LO2/LCH4 Reaction Control Engine (RCE) aimed at reducing the risk of utilizing a cryogenic reaction control system (RCS) on a space vehicle. Aerojet utilized innovative design solutions to develop an RCE that can ignite reliably over a broad range of inlet temperatures, perform short minimum impulse bits (MIB) at small electrical pulse widths (EPW), and produce excellent specific impulse (Isp) across a range of engine mixture ratios (MR). These design innovations also provide a start transient with a benign MR, ensuring good thrust chamber compatibility and long life. In addition, this RCE can successfully operate at MRs associated with main engines, enabling the RCE to provide emergency backup propulsion to minimize vehicle propellant load and overall system mass.
Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative Jack of scalability and efficiency of typical prognostics approaches. In order to address these is ues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resul ting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.
The numerous benefits of automatic application code generation are widely accepted within the software engineering community. A few of these benefits include raising the abstraction level of application programming, shorter product development time, lower maintenance costs, and increased code quality and consistency. Surprisingly, code generation concepts have not yet found wide acceptance and use in the field of programmable logic controller (PLC) software development. Software engineers at the NASA Kennedy Space Center (KSC) recognized the need for PLC code generation while developing their new ground checkout and launch processing system. They developed a process and a prototype software tool that automatically translates a high-level representation or specification of safety critical application software into ladder logic that executes on a PLC. This process and tool are expected to increase the reliability of the PLC code over that which is written manually, and may even lower life-cycle costs and shorten the development schedule of the new control system at KSC. This paper examines the problem domain and discusses the process and software tool that were prototyped by the KSC software engineers.
Advanced ACTPol (Adv ACT) will use an array of multichroic polarization sensitive AIMn transition edge sensor (TES) bolometers read out through time-division multiplexing. Aluminum doped with a low concentration of manganese can be deposited to a bulk film thickness for a more reliable superconducting critical temperature uniformity compared to thin bilayers. To build the TES, the AlMn alloy is deposited, over Nb wiring, to a specific thickness to set the TES normal resistance. The doping concentration of manganese coarsely defines the TES critical temperature, while a fine tuning is achieved by heating the deposited film to a specific temperature. The TES island is connected to the thermal bath via four silicon-nitride membranes, where their geometry defines the thermal conductance to the temperature of the bath. Lastly, the TES heat capacity is increased by addition of PdAu electrically connected to the AlMn film. Designs and performance characteristics of these AlMn TESs are presented for use in AdvACT.
Launch vehicle systems are designed and developed using both heritage and new hardware. Design modifications to the heritage hardware to fit new functional system requirements can impact the applicability of heritage reliability data. Risk estimates for newly designed systems must be developed from generic data sources such as commercially available reliability databases using reliability prediction methodologies, such as those addressed in MIL-HDBK-217F. Failure estimates must be converted from the generic environment to the specific operating environment of the system in which it is used. In addition, some qualification of applicability for the data source to the current system should be made. Characterizing data applicability under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This paper will demonstrate a data-source applicability classification method for suggesting epistemic component uncertainty to a target vehicle based on the source and operating environment of the originating data. The source applicability is determined using heuristic guidelines while translation of operating environments is accomplished by applying statistical methods to MIL-HDK-217F tables. The paper will provide one example for assigning environmental factors uncertainty when translating between operating environments for the microelectronic part-type components. The heuristic guidelines will be followed by uncertainty-importance routines to assess the need for more applicable data to reduce model uncertainty.
Launch vehicle systems are designed and developed using both heritage and new hardware. Design modifications to the heritage hardware to fit new functional system requirements can impact the applicability of heritage reliability data. Risk estimates for newly designed systems must be developed from generic data sources such as commercially available reliability databases using reliability prediction methodologies, such as those addressed in MIL-HDBK-217F. Failure estimates must be converted from the generic environment to the specific operating environment of the system where it is used. In addition, some qualification of applicability for the data source to the current system should be made. Characterizing data applicability under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This paper will demonstrate a data-source applicability classification method for assigning uncertainty to a target vehicle based on the source and operating environment of the originating data. The source applicability is determined using heuristic guidelines while translation of operating environments is accomplished by applying statistical methods to MIL-HDK-217F tables. The paper will provide a case study example by translating Ground Benign (GB) and Ground Mobile (GM) to the Airborne Uninhabited Fighter (AUF) environment for three electronic components often found in space launch vehicle control systems. The classification method will be followed by uncertainty-importance routines to assess the need to for more applicable data to reduce uncertainty.
In the future higher temperature Stirling convertors for improved efficiency and performance, various high temperature organic materials have been demanded as essential components for their unique properties and functions such as bonding, potting, sealing, thread locking, insulation, and lubrication. The higher temperature capabilities would also allow current state-of-the-art (SOA) convertors to be used in additional missions, particularly those that require a Venus flyby for a gravity assist. Stirling convertor radioisotope generators have been developed for potential future space applications including Lunar/Mars surface power or a variety of spacecraft and vehicles, especially with a long mission cycle, sometimes up to 17 years, such as deep space exploration. Thus, performance, durability, and reliability of the organics should be critically evaluated in terms of comprehensive structure-process-service environment relations based on the potential mission specifications. The initial efforts in screening the high temperature candidates focused on the most susceptible organics, such as adhesive, potting compound, o-ring, shrink tubing, and thread locker materials in conjunction with commercially available materials. More systematic and practical test methodologies that were developed and optimized based on the extensive organic evaluations and validations performed for various Stirling convertor types were employed to determine thermal stability, outgassing, and material compatibility of the selected organic candidates against their functional requirements. Processing and fabrication conditions and procedures were also optimized. This paper presents results of the three-step candidate evaluation processes, their application limitations, and the final selection recommendations.
The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO provided global measurements of attenuated backscatter profiles of various tropospheric aerosol species from June 2006 through June 2023. Extinction profiles are retrieved from these backscatter measurements by assuming a value for the extinction-to-backscatter or lidar ratio (LR). Up until version V4.51 of the data products, only a constant global value for each of the species has been used, spatially and temporally. However, it has been well known from various ground-based measurements that LRs of each aerosol species can potentially vary spatially as well seasonally. For the forthcoming Version 5 of the CALIPSO products, the CALIPSO team will implement variable lidar ratios at 532 nm for the species classified as “marine” and “dusty marine”. Here we present the essential elements of this scheme, which involves estimating lidar ratios by constraining Fernald solutions for the CALIOP particulate backscatter profiles using aerosol optical depths (AOD) from collocated MODIS retrievals. To account for the lack of measurements in various regions and seasons (e.g., Arctic winters), we leverage collocated model sea-salt volume fractions (SSVF) simulated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and use an empirical relationship between the SSVF and LR to build hybrid climatological maps for the entire globe, i.e., using the modelled values when reliable values of the constrained LRs are not available. Initial evaluation of the resulting extinction profiles and corresponding AODs, both globally and in specific regions are presented using a limited amount of data. The primary improvement in AOD occurs in the coastal areas, like the Arabian Sea and Bay of Bengal. The estimated marine LR values in these regions are significantly higher than the constant value of 23 sr used in previous data releases, likely due to mixing with offshore pollution which results in higher AODs. Initial validation results using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) retrievals and ground-based AERONET AOD measurements are presented.
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>
Broad commercialization of solid oxide fuel cells (SOFCs), requires anode offgas recycle blowers (AORB) that are specifically designed for handling the challenging operating conditions presented by the SOFC process gases. Otherwise, they can be susceptible to frequent maintenance, low reliability, and short life. This work presents details of the development and testing of an oil-free, low cost centrifugal AORB based on compliant foil bearing (CFB) technology for support of a 100 kW solid oxide fuel cell power plant. The advantages of this novel technology are discussed, particularly its long life and maintenance-free operation, which are derived from the use of CFBs. Also included are preliminary techno-economic analysis considerations for cost-effective deployment of the technology.
Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.