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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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At least 253 records · Page 14

Space transportation main engine cycle assessment process

The Advanced Launch System (ALS) program selection process for a space transportation main engine (STME) power cycle is described in terms of the methodology employed. Low cost, robustness, and high reliability are the primary parameters for engine choice, suggesting simplicity of design and efficient fabrication methods as the crucial characteristics. An evaluation methodology is developed based on the Pugh (1981) process and the King (1989) matrices. The cycle configurations considered are the gas generator (GG), the closed expander, and the open expander. The cycle assessment team determined that the GG cycle is favored by most cycle discriminators, based on an assessment of the characteristics in terms of ALS goals. The lower development risk of the GG-cycle STME is consistent with the goals of the ALS program in terms of reliability and cost efficiency.

Mcconnaughey, H. V.↗

High Temperature Optocoupler for 3D High Density Power Modules

The goal of this proposed research is to develop a reliable high-temperature optocouplers, which can operate at 250°C with at least ten-year lifetime, and replace isolation transforms as the galvanic isolation solution for the 3D integration of high density power modules. The electrification of future transportations (i.e., electric vehicles) will continuously drive the demand for high density power modules. Optocouplers (i.e., packaged light emitter and detector) as a promising candidate to replace bulky isolation transformers are highly desirable to facilitate the continuous scale-down of gate driver circuitry that will lead to 3D high density power modules and achieve disruptive performance in terms of thermal management, power density, power efficiency, reliability and operating environments. However, regular semiconductor optoelectronic materials and devices have significant difficulty functioning in the harsh environments designated for high density power module usage (such as operation at high temperatures). Ultimately, it is not the intrinsic properties of power devices that prevent their use at higher temperatures, but rather the low voltage electronics needed to drive them and the packaging that surrounds them. The typical operating temperature for optocouplers is only up to 100°C, due to the limitations of light emitting diode (LED) devices inside and packaging materials. A systematic characterization methodology will be developed to analyze the performance, lifetime and reliability of LED devices and distinguish multiple failure mechanisms at high temperatures. An original methodology of “design for reliability” will be developed to design the optoelectronic devices with high reliability and long lifetime at high temperatures. A new architecture of high temperature high reliable optocouplers will be developed, fabricated and demonstrated with continuous operating at 250°C. The development of efficient, reliable high density 3D power modules is the foundation for energy efficiency and energy reliability. Enabled with advanced 3D integration and packaging technologies, high density power module solutions can achieve much more superior performance over the conventional discrete solutions in terms of efficiency, thermal management and power density. The proposed concept of high temperature optocouplers as the galvanic isolation solution for high density power modules will bring together interdisciplinary research involving the wide bandgap materials, optoelectronics, high reliable device design, electronics packaging and power modules. A streamline of skilled personnel would be trained including graduate and undergraduate students, local engineers and scientists which are in great demand to both academia and optoelectronics industry. The proposed research topics, such as, solid state lighting and high temperature device reliability, are currently of major interest at the Department of Energy, in particular, Sandia National Laboratories. This project can enhance collaborations between the University of Arkansas (UA) and Sandia National Laboratories. The findings of the proposed research are expected to be integrated into high density 3-D power modules at the Engineering Research Center for Power Optimization for Electro-Thermal Systems (POETS).

42 ENGINEERING↗

Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics

Numerical simulation has now become an integral part of engineering design process. Critical design decisions are routinely made based on the simulation results and conclusions. Verification and validation of the reliability of the numerical simulation is therefore vitally important in the engineering design processes. We propose to develop theories and methodologies that can automatically provide quantitative information about the reliability of the numerical simulation by estimating numerical approximation error, computational model induced errors and the uncertainties contained in the mathematical models so that the reliability of the numerical simulation can be verified and validated. We also propose to develop and implement methodologies and techniques that can control the error and uncertainty during the numerical simulation so that the reliability of the numerical simulation can be improved.

Zhu, J. Z.↗

Vulcan Test Platform: Demonstrating the Data Center as a Flexible Grid Asset

Explosive data center demand is outpacing grid infrastructure development. AI workloads and hyperscale cloud growth are creating unprecedented power requirements, while traditional grid expansion faces multi-year development timelines, regulatory hurdles, and decarbonization challenges. Sidewalk Infrastructure Partners recognized this impending crisis years ago and founded Verrus to develop an innovative solution: data centers that function as grid assets rather than passive loads. The Verrus approach integrates proprietary grid-aware controls with battery energy storage systems (BESS) in a medium-voltage architecture that delivers three critical capabilities: Fast-responding demand flexibility that can service requests from the utility within 10 seconds, Uninterrupted transition to islanded operation during grid disturbances, Continuous uptime assurance while maintaining all customer service level commitments Through Verrus' strategic partnership with the National Renewable Energy Laboratory (NREL), we validated these capabilities on Vulcan, a 70 MW utility-scale test platform powered by NREL's ARIES Virtual Emulation Environment. This deployment-ready technology has successfully demonstrated that Verrus data centers can deliver meaningful grid services while maintaining mission-critical reliability. This technical report outlines the design, methodology, and results of this emulated deployment, demonstrating that data centers can play a pivotal role in enhancing grid flexibility and reliability, without sacrificing service level guarantees.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Reliability-based optimization under random vibration environment

A methodology of formulating the optimum design problem for structural systems with random parameters and subjected to random vibration as a mathematical programming problem is presented. The proposed method is applied to the optimum design of a cantilever beam with a tip mass and a truss structure supporting a water tank. The excitations are assumed to be Gaussian processes and the geometric and material properties are taken to be normally distributed random variables. The probabilistic constraints are specified for individual failure modes since it is easier to specify the reliability level for each failure mode keeping in view the consequences of failure in that particular mode. The time parameter appearing in the random vibration based constraints is eliminated by replacing the probabilities of failure by suitable upper bounds. The numerical results demonstrate the feasibility and effectiveness of applying the reliability-based design concepts to structures with random parameters and operating in random vibration environment.

Rao, S. S.↗

Risk-informed Graded Approach for Reliability and Performance Assessment of Machine Learning and Artificial Intelligence for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

97 - MATHEMATICS AND COMPUTING↗

Risk-informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

99 - GENERAL AND MISCELLANEOUS↗

Probabilistic Sizing of Energy Storage Systems for Reliability and Frequency Security in Wind-Rich Power Grids

The penetration of wind energy has increased significantly in the power grid in recent times. Although wind is abundant, environment-friendly, and cheap, it is variable in nature and does not contribute to system inertia as much as conventional synchronous generators. Coupled with the low inertia contribution, the generation intermittency of wind power leads to reliability and stability issues in the power system. Energy storage systems (ESSs) are among the most prominent alternatives to alleviate these concerns associated with high wind penetration. This paper proposes a planning strategy to size ESS for the reliability and frequency security of wind-rich power grids. A probabilistic methodology for ESS sizing is developed utilizing a composite reliability-based framework with sequential Monte Carlo simulation (MCS). The MCS generates composite reliability indices for the power system, which are employed to obtain the capacity for a reliability energy storage system (RESS). Simultaneously, the MCS-derived probability of synchronization of conventional generators is integrated into an analytical approach for sizing a frequency support energy storage system (FESS). The effect of wind farm dispersion across geographical regions is incorporated in the framework to study possible reductions in the ESS size while maintaining the system reliability and frequency security. Furthermore, the efficacy of the proposed strategy is demonstrated on the RTS-GMLC test system.

25 ENERGY STORAGE↗

The cost of unreliability in the avionics suite of a launch vehicle

The main objective of the Advanced Launch System (ALS) program is to realize a substantial reduction in recurring launch costs over present launch systems. A methodology is presented for assessing the impact of the reliability of the avionics suite on the recurring launch cost of the ALS. The methodology is illustrated by focusing on two architectures. The first is a distillation of a typical architecture for this type of application. The second is a simplified implementation of the Advanced Information Processing System (AIPS) technology developed at the Charles Stark Draper Laboratory, Inc. Both architectures utilize redundancy of hardware to tolerate faults. the key contributors to the cost of unreliability are identified and modeled utilizing Markov modeling techniques for each of the two architectures. The results are presented along with the more traditional costs for these avionics suites.

Rosch, Gene↗

Transformative remedial action scheme tool (TRAST)

Techniques and apparatuses are described that enable transformative Remedial Action Scheme (RAS) analyses and methodologies for a bulk electric power system, including methods of designing, reviewing, revising, testing, implementing, verifying, or validating a RAS. An improved RAS improves operation of the power system, including performance, reliability, control, and asset utilization. The example methodologies discussed—also referred to as a transformative Remedial Action Scheme tool (TRAST)—provide an end-to-end solution for adaptively setting RAS parameters based on realistic and near real-time operation conditions to improve power grid reliability and grid asset utilization, by leveraging utility data analysis and employing dynamic simulations and machine learning to significantly simplify and shorten the entire RAS process.

Fan, Xiaoyuan↗

On reliable control system designs

A research effort is summarized which addresses some of the current problems in interfacing systems theory and reliability. Reliability is roughly the probability that a system will perform according to specifications for a given amount of time. The reliability of a system depends on the structure of its components. Systems theory and control theory deal with the response characteristics of a system, which depend on the system dynamics. The concepts necessary to unify the structural and the dynamic properties of a system are defined. The result is a definition of what constitutes a reliable systems, from the viewpoint of systems theory, and a methodology which can be used to determine if a given design allows a reliable control system design.

Birdwell, J. Douglas↗

On the relationship between reliability and linear quadratic optimal control

The linear quadratic optimal control method is used today to solve many complex systems problems. As system complexity increases, and as linear quadratic optimal control is used in more demanding situations, the extension of the design methodology to cover system failures, robustness and reliability is of crucial importance. This paper documents the progress toward a theory which incorporates reliability in the performance index; a linear quadratic control problem is formulated which accounts for system effectiveness and gives an offline procedure for comparing two linear quadratic control systems on the basis of both reliability and performance.

Birdwell, J. D.↗

Integrated multimodel analysis reveals achievable pathways toward reliable, 100% renewable electricity for Los Angeles

Climate change has prompted many communities to set targets for carbon-free power supplies, but they often lack data-driven strategies to achieve them. We present a comprehensive analysis of an entirely renewable electric power system that can maintain operating reliability and resource adequacy using detailed models of the city of Los Angeles power grid. In consultation with the operating utility, the Los Angeles Department of Water and Power (LADWP), and the local community, we develop four supply scenarios across three demand projections to analyze which types of infrastructure and operational changes would achieve reliable electricity at least cost. We find that a reliable, 100%-renewable power system yielding more than $1 billion annually in health and climate co-benefits is achievable. Solar can supply most future energy needs, while combustion turbines that use renewable, storable carbon-neutral fuels are key to maintaining reliability. This study provides a replicable methodology that other jurisdictions globally can follow.

14 SOLAR ENERGY↗

A decomposition method for optimization of large-system reliability

This paper considers a decomposition approach for optimization of the reliability of a large system with a general network structure. A 3-level methodology is developed for optimal allocation of available resources among subsystems in order to ensure maximization of system-reliability. The decentralized nature of this methodology greatly reduces the complexity of the large problem and facilitates seeking the optimal solution. Two examples show that the complexity of a large system can be greatly reduced by solving several smaller-dimensional sub-problems iteratively. Sub-problems whose dimensions are small can be efficiently solved by any existing nonlinear programming method. Another important feature of this approach is the possible simplification of the objective function during the solution. This leads in some cases to an analytic solution for the lower-level optimization problems in a 3-level decomposition solution.

Li, Duan↗

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗

Mitigation of Aerosol Impacts of Ash Deposition and Emissions from Coal Combustion (Final Technical Report)

This project was selected under FOA-0001989 Area of Interest (AOI) 2 – Power Plant Component Improvement. The objective of this AOI was to test and validate technologies and methodologies that can improve the efficiency, reliability, and/or flexibility of individual power plant components in existing plants. The aerosol mitigation technology comprises injection of sorbent into the boiler through injection lances using a positive-pressure blower. The injected sorbent interacts with flame-vaporized aerosolforming components produced during combustion reducing the abundance of sub-micrometer (< 1 micron) particles and fireside ash deposits in the boiler. The primary objective of this project was to validate a sorbent-based technology for mitigating fireside heat transfer surface fouling-related challenges in a full-scale coal-fired power generation facility, thereby, improving plant performance and reliability.

01 COAL, LIGNITE, AND PEAT↗

Systems design study of the Pioneer Venus spacecraft. Volume 2. Preliminary program development plan

The preliminary development plan for the Pioneer Venus program is presented. This preliminary plan treats only developmental aspects that would have a significant effect on program cost. These significant development areas were: master program schedule planning; test planning - both unit and system testing for probes/orbiter/ probe bus; ground support equipment; performance assurance; and science integration Various test planning options and test method techniques were evaluated in terms of achieving a low-cost program without degrading mission performance or system reliability. The approaches studied and the methodology of the selected approach are defined.

Source record↗

Chemical production on Mars using in situ propellant production technology

In situ propellant production (ISPP) was examined in terms of its applicability to a manned Mars mission. Production of oxygen from Martian atmosphere was used as the baseline system for ISPP technology assessment. It was concluded that production of oxygen was an important element in a manned Mars mission which could be developed in terrestrial laboratories. Expert system methodology will be required to enable reliable, autonomous production of oxygen. Furthermore, while no major technical breakthroughs are required, this research requires a long lead time to permit its systematic evolution.

Robert L Ash↗