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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 811 records · Page 45

Deliverable 6.7-Final Technical Report: Development Summary and Evaluation of the Solar Uncertainty Integrator (SUNI) Software

The Data Quality and Uncertainty Integration Project was a three-year effort to address stakeholder needs for assessing solar radiation resource data quality based on existing tools for estimating radiometer measurement uncertainties and assessing post-measurement data quality. The annual research objectives for the project addressed a logical progression of effort needed to achieve the ultimate project goal of developing the Solar Uncertainty Integrator (SUNI) software. This final technical report summarizes the development process for achieving these key research objectives and addresses the outreach and code development efforts in the final year of the project to develop a new solar irradiance data uncertainty integration software package.

14 SOLAR ENERGY↗

Numerical prediction of axial turbine stage aerodynamics

A preliminary assessment is made of two NASA-developed unsteady turbine stage computer codes. The methodology and previous partial validation of the codes are briefly outlined. Application of these codes to a Space Shuttle main engine turbine for two sets of operating conditions is then described. Steady and unsteady, two and three-dimensional results are presented, compared, and discussed. These results include time-mean and instantaneous airfoil pressure distributions and pressure fluctuations, streamlines on the airfoil surfaces and endwalls, and relative total pressure contours at different axial locations in the rotor passage. Although not available at the time of this writing, experimental data for one of the operating conditions simulated is forthcoming and will be used to assess the accuracy of the unsteady, as well as, the steady predictions presented. Issues related to code usage and resource requirements of the two codes are also discussed.

Mcconnaughey, H. V.↗

NASA Center for Climate Simulation (NCCS) Presentation

The NASA Center for Climate Simulation (NCCS) offers integrated supercomputing, visualization, and data interaction technologies to enhance NASA's weather and climate prediction capabilities. It serves hundreds of users at NASA Goddard Space Flight Center, as well as other NASA centers, laboratories, and universities across the US. Over the past year, NCCS has continued expanding its data-centric computing environment to meet the increasingly data-intensive challenges of climate science. We doubled our Discover supercomputer's peak performance to more than 800 teraflops by adding 7,680 Intel Xeon Sandy Bridge processor-cores and most recently 240 Intel Xeon Phi Many Integrated Core (MIG) co-processors. A supercomputing-class analysis system named Dali gives users rapid access to their data on Discover and high-performance software including the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), with interfaces from user desktops and a 17- by 6-foot visualization wall. NCCS also is exploring highly efficient climate data services and management with a new MapReduce/Hadoop cluster while augmenting its data distribution to the science community. Using NCCS resources, NASA completed its modeling contributions to the Intergovernmental Panel on Climate Change (IPCG) Fifth Assessment Report this summer as part of the ongoing Coupled Modellntercomparison Project Phase 5 (CMIP5). Ensembles of simulations run on Discover reached back to the year 1000 to test model accuracy and projected climate change through the year 2300 based on four different scenarios of greenhouse gases, aerosols, and land use. The data resulting from several thousand IPCC/CMIP5 simulations, as well as a variety of other simulation, reanalysis, and observationdatasets, are available to scientists and decision makers through an enhanced NCCS Earth System Grid Federation Gateway. Worldwide downloads have totaled over 110 terabytes of data.

Webster, William P.↗

A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Comparing three generations of D-Wave quantum annealers for minor embedded combinatorial optimization problems

Abstract Quantum annealing (QA) is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. QA in the transverse Ising model, implemented on D-Wave quantum processing units, are available as cloud computing resources. In this study we report concise benchmarks across three generations of D-Wave quantum annealers, consisting of four different devices, for the NP-hard discrete combinatorial optimization problems unweighted maximum clique and unweighted maximum cut on random graphs. The Ising, or equivalently quadratic unconstrained binary optimization, formulation of these problems do not require auxiliary variables for order reduction, and their overall structure and weights are not highly variable, which makes these problems simple test cases to understand the sampling capability of current D-Wave quantum annealers. All-to-all minor embeddings of size 52, with relatively uniform chain lengths, are used for a direct comparison across the Chimera, Pegasus, and Zephyr device topologies. A grid-search over annealing times and the minor embedding chain strengths is performed in order to determine the level of reasonable performance for each device and problem type. Experiment metrics that are reported are approximation ratios for non-broken chain samples, chain break proportions, and time-to-solution for the maximum clique problem instances. How fairly the quantum annealers sample optimal maximum cliques, for instances which contain multiple maximum cliques, is quantified using entropy of the measured ground state distributions. The newest generation of quantum annealing hardware, which has a Zephyr hardware connectivity, performed the best overall with respect to approximation ratios and chain break frequencies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The AI Bus architecture for distributed knowledge-based systems

The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.

Schultz, Roger D.↗

Real-Time Hardware-in-The-Loop Implementation of Protection and Self-Healing of Microgrids

The design of a reliable protection scheme for microgrids often requires communication between protective devices and microgrid controllers. The authors have developed such a communication assisted scalable protection scheme with a self healing feature to protect a microgrid with 100% Inverter Based Resources (IBRs). The communication assisted scheme must be validated in real time with cyber physical co-simulation for successful demonstration. In this regard, the paper presents a co-simulation platform between a simulated power system model using RTDS and physical protective devices. The primary protection of the scheme is programmed in SEL 421-7 relay and backup protection is programmed in MATLAB on a generic computer acting as a microgrid controller. The IEC 61850 models are used to communicate between SEL-421-7 relay and RTDS, whereas TCP/IP communication connects the microgrid controller to RTDS. The paper’s focus is to demonstrate the co- simulation platform with communication links established using both protocols and validate the proposed scheme in real-time on the IEEE 123 node distribution feeder. The paper explains the configuration of the IEC 61850 and TCP/IP communications as the interface requires proper hardware and software setup. Furthermore, the real time performance indicates the Hardware In the Loop (HIL) framework as a competent testing environment for the developed protection scheme for microgrids.

42 ENGINEERING↗

Universal Utility Data Exchange (UUDEX): Information Exchange Models for Solar DER

This report describes how the Universal Utility Data Exchange (UUDEX) can encapsulate and exchange information about distributed energy resources (DER) within UUDEX-formatted JSON documents. It proposes to use the existing extensible markup language (XML) information exchange format described in IEEE Std 2030.5, IEEE Standard for Smart Energy Profile Application Protocol but not other information exchange provisions of IEEE 2030.5, rather, relying on equivalent mechanisms in UUDEX. This will allow adoption of the UUDEX exchanges with minimal changes to the structure of the information exchanged and will not require a modification to the UUDEX documentation if IEEE 2030.5 makes changes to the information models.

42 ENGINEERING↗

An optimization model for energy generation and distribution in a dynamic facility

An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

Lansing, F. L.↗

Dynamic Distribution System Restoration Strategy for Resilience Enhancement: Preprint

In electric power distribution systems, distributed energy re-sources (DERs) can act as controllable power sources and support utility operators to minimize power outages after ex-treme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influ-ences of extreme events and the capabilities of DERs are dy-namic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availabil-ity of DERs. In this paper, we propose a new dynamic distribu-tion system restoration strategy to enhance system resilience against potential hazards. An efficient reconfiguration algo-rithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER opera-tion setpoints based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗

Content and Representation of Information Needed to Support Time-Constrained Problem Solving

NASA’s current mission-operations paradigm originated with Project Mercury and endured with minimum evolution through the Apollo Program, Space Shuttle Program, and ISS missions. At its foundation is a near-complete real-time dependence on a ground team to manage the combined state of the mission, vehicle, and crew. Utilizing many engineers and operators with broad and deep expertise; large, distributed datasets including extensive telemetry; and expansive analytical and computing power, this ground team has served as the safety net for crewed spaceflight missions over the past 60 years. This approach must change to address challenges associated with missions beyond low Earth orbit (BLEO), including infrequent resupply, reduced ability to evacuate, and delayed communications that prohibit real-time operational support. We anticipate that a necessary part of this change will be increased independence for the crew, as roles and responsibilities traditionally performed by ground teams move on board the vehicle. While many risks are associated with Earth-independent operations, one particular concern is ensuring that the crew will have adequate onboard support to perform urgent problem solving when communication with the ground is delayed or intermittent. A key resource that enables the ground team to respond to anomalies quickly and effectively is the extraordinary expertise and experience it possesses. It is comprised of 80+ experts on at any given time, with a combined 600+ years of system-specific experience across 22 unique console disciplines. A small crew will face the unprecedented challenge of independently responding to anomalies that have historically been handled by a team 20 times their size. Another important resource upon which the ground heavily relies to support procedure execution and anomaly response is data. The amount of telemetry data that each flight controller monitors is extensive. In addition, as the ground team works to further assess impacts, trouble shoot, identify workarounds, and oversee procedure execution, it accesses and synthesizes engineering and procedure information, as well as system build, test, and configuration documentation. It is not feasible nor useful to put all these data onboard as crews become more Earth independent. Each member of a small Mars mission small crew will have multiple roles beyond monitoring telemetry and data gathering, and multiple roles within anomaly resolution processes, thereby limiting their capacity for copious amounts of information. Moreover, while access is necessary, it alone is insufficient. Information will need to be compiled, refined, and represented appropriately to support the crew’s reduced attention and expertise. This work seeks to understand the content and representation of information needed to support time-constrained problem solving and decision making by the crew without real-time ground support. To build this understanding, we first surveyed the literature, focusing on how expert problem solvers construct and manipulate their mental models. Next, we interviewed expert problem solvers in spaceflight and analogous domains and surveyed industry solutions for data presentation. Finally, we analyzed current spaceflight operations by investigating flight controller anomaly resolution processes during ISS training simulations and real operational events. These methods led to creating a problem-solving framework that details common themes and features of attending to, assessing, analyzing, and acting on problems in complex, time-constrained domains. Using this framework and the results of our analysis, we identified conceptual data representations needed for crew-led problem-solving. Preliminary onboard user interface concepts to meet identified needs will be presented.

anomaly response↗

Thermal Overloading Risk Mitigation With a Semi-Analytical Probabilistic Model on Branch Current

A semi-analytical formulation is presented in this paper for the probability computation of branch current in multiphase systems. The developed formula is derived based on the linear power flow model in rectangular coordinates. The system uncertainty injections can be renewable energy resources or loads and are modeled using a Gaussian mixture model (GMM). The developed formula can be used to compute the line current violation probability as well as integrate into optimal power flow problem as chance-constraint relaxation. Here, the proposed formula is first compared with the Matlab embedded numerical integration function to show its performance. Besides, the semi-analytical formula is validated and compared with the Monte Carlo simulation method using the IEEE 123-bus system, EPRI Ckt5, and Ckt7 systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Universal Utility Data Exchange (UUDEX): Information Exchange Models for Solar DER (Rev. 1)

This report describes how the Universal Utility Data Exchange (UUDEX) can encapsulate and exchange information about distributed energy resources (DER) within UUDEX-formatted JSON documents. It proposes to use the existing extensible markup language (XML) information exchange format described in IEEE Std 2030.5, IEEE Standard for Smart Energy Profile Application Protocol but not other information exchange provisions of IEEE 2030.5, rather, relying on equivalent mechanisms in UUDEX. This will allow adoption of the UUDEX exchanges with minimal changes to the structure of the information exchanged and will not require a modification to the UUDEX documentation if IEEE 2030.5 makes changes to the information models.

14 SOLAR ENERGY↗

Dynamic Matrix Completion Based State Estimation in Distribution Grids

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this paper, we propose two dynamic sparsity-based state estimation approaches for distribution systems: (1) locally weighted matrix completion (LW-MC) and (2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Finally, results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

42 ENGINEERING↗

ResStock: Annual Baseline Results with Component Loads

The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities. The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel. A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.

Array↗

Streaming Data Reorganization at Scale with DeltaFS Indexed Massive Directories

We report complex storage stacks providing data compression, indexing, and analytics help leverage the massive amounts of data generated today to derive insights. It is challenging to perform this computation, however, while fully utilizing the underlying storage media. This is because, while storage servers with large core counts are widely available, single-core performance and memory bandwidth per core grow slower than the core count per die. Computational storage offers a promising solution to this problem by utilizing dedicated compute resources along the storage processing path. We present DeltaFS Indexed Massive Directories (IMDs), a new approach to computational storage. DeltaFS IMDs harvest available (i.e., not dedicated) compute, memory, and network resources on the compute nodes of an application to perform computation on data. We demonstrate the efficiency of DeltaFS IMDs by using them to dynamically reorganize the output of a real-world simulation application across 131,072 CPU cores. DeltaFS IMDs speed up reads by 1,740x while only slightly slowing down the writing of data during simulation I/O for in situ data processing.

97 MATHEMATICS AND COMPUTING↗