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This presentation provides an overview of the laboratory evaluation of federated, hierarchical controls for distribution power system management.
We propose an analytical construction of observable functions in the extended dynamic mode decomposition (EDMD) algorithm. EDMD is a numerical method for approximating the spectral properties of the Koopman operator. The choice of observable functions is fundamental for applying EDMD to nonlinear problems arising in systems and control. Existing methods either start from a set of dictionary functions and look for the subset that best fits the underlying nonlinear dynamics or rely on machine learning algorithms to “learn” observable functions. Conversely, in this paper, we start from the dynamical system model and lift it through the Lie derivatives, rendering it into a polynomial form. This proposed transformation into a polynomial form is exact and provides an adequate set of observable functions. The strength of the proposed approach is its applicability to a broader class of nonlinear dynamical systems, particularly those with nonpolynomial functions and compositions thereof. Moreover, it retains the physical interpretability of the underlying dynamical system and can be readily integrated into existing numerical libraries. We demonstrate the proposed approach with an application to electric power systems. The modeled system consists of a single generator connected to an infinite bus, where nonlinear terms include sine and cosine functions. The results demonstrate the effectiveness of the proposed procedure in off-attractor nonlinear dynamics for estimation and prediction; the observable functions obtained from the proposed construction outperform methods that use dictionary functions comprising monomials or radial basis functions.
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The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience.
Tensor algebra is a powerful tool for computing, but writing optimized codes that operate on sparse tensors can be very complex. This project enables a Tensor Algebra Compiler (TACO) that simplifies this task from man-years to man-days and extends TACO to support complex and large distributed systems. This report details the hypotheses, approaches used, and findings in this project.
Future space missions will require more sophisticated power systems, implying higher costs and more extensive crew and ground support involvement. To decrease this human involvement, as well as to protect and most efficiently utilize this important resource, NASA has undertaken major efforts to promote progress in the design and development of autonomously managed power systems. Two areas being actively pursued are autonomous power system (APS) breadboards and knowledge-based expert system (KBES) applications. The former are viewed as a requirement for the timely development of the latter. Not only will they serve as final testbeds for the various KBES applications, but will play a major role in the knowledge engineering phase of their development. The current power system breadboard designs are of a distributed microprocessor nature. The distributed nature, plus the need to connect various external computer capabilities (i.e., conventional host computers and symbolic processors), places major emphasis on effective networking. The communications and networking technologies for the first power system breadboard/test facility are described.
This report summarizes our research activities within the TEAM project between December 2020 and December 2021, funded by the ASCR Advanced Research in Quantum Computing program. During the reporting period the LLNL-MSU team has made progress on several fronts. An overarching goal of the team is to provide a comprehensive suite of software tools that can be used for the Characterize-Optimize-Compute loop needed to implement and execute algorithms on quantum devices. We are concurrently developing lightweight solvers that can be used on desktop computers to find optimal control pulses and to characterize small quantum systems (consisting of a few transmons and cavities). However, desktop computers are insufficient for simulating and characterizing larger quantum systems. We have therefore also developed parallel, distributed memory, simulators and optimization solvers, both for open and closed quantum systems. These parallel solvers have, for example, been used to study quantum optimal control for pure-state preparation, utilizing 1000’s of cores on a modern high-performance computing (HPC) platform.
Robot systems in critical applications, such as those in space and nuclear environments, must be able to operate during component failure to complete important tasks. One failure mode that has received little attention is the failure of joint position sensors. Current fault tolerant designs require the addition of directly redundant position sensors which can affect joint design. A new method is proposed that utilizes analytical redundancy to allow for continued operation during joint position sensor failure. Joint torque sensors are used with a virtual passive torque controller to make the robot joint stable without position feedback and improve position tracking performance in the presence of unknown link dynamics and end-effector loading. Two Cartesian accelerometer based methods are proposed to determine the position of the joint. The joint specific position determination method utilizes two triaxial accelerometers attached to the link driven by the joint with the failed position sensor. The joint specific method is not computationally complex and the position error is bounded. The system wide position determination method utilizes accelerometers distributed on different robot links and the end-effector to determine the position of sets of multiple joints. The system wide method requires fewer accelerometers than the joint specific method to make all joint position sensors fault tolerant but is more computationally complex and has lower convergence properties. Experiments were conducted on a laboratory manipulator. Both position determination methods were shown to track the actual position satisfactorily. A controller using the position determination methods and the virtual passive torque controller was able to servo the joints to a desired position during position sensor failure.
GFSSP stands for Generalized Fluid System Simulation Program. It is a general-purpose computer program to compute pressure, temperature and flow distribution in a flow network. GFSSP calculates pressure, temperature, and concentrations at nodes and calculates flow rates through branches. It was primarily developed to analyze Internal Flow Analysis of a Turbopump Transient Flow Analysis of a Propulsion System. GFSSP development started in 1994 with an objective to provide a generalized and easy to use flow analysis tool for thermo-fluid systems.
This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.
In this work, we present a software package in Python for high-throughput first-principles calculations of thermodynamic properties at finite temperatures, which we refer to as DFTTK (Density Functional Theory ToolKit). DFTTK is based on the atomate package and integrates our experiences in the last decades on the development of theoretical methods and computational softwares. It includes task submissions on all major operating systems and task executions on high-performance computing environments. Furthermore, the distribution of the DFTTK package comes with examples of calculations of phonon density of states, heat capacity, entropy, enthalpy, and free energy under the quasi-harmonic phonon scheme for the stoichiometric phases of Al, Ni, Al 3 Ni, AlNi, AlNi 3 , Al 3 Ni 4 , and Al 3 Ni 5 , and the fcc solution phases treated using the special quasirandom structures at the compositions of Al 3 Ni, AlNi, and AlNi 3 .
The inverter-based resource (IBR) has become avery important component in the distribution system. The impacts on system transient stability introduced by high IBR penetration are not fully addressed because of the lack of high-fidelity models. The aggregate IBR model at the transmission level cannot precisely reproduce the dynamics of distributed IBR at the distribution system because of the oversimplification. In this paper, we will develop a high-penetration fully-connected transmission and distribution (T&D) co-simulation platform that supports the simulation of 10,000+ dispersed IBR models. The interfacing and iterative initialization techniques for the co-simulation have been implemented to maintain stable operation and simulation of large-multitude of IBR models. The phasor-domain IBR models with grid-forming (GFM) and grid-following (GFL) control are implemented in the distribution systems simulators. The developed platform is tested on high-performance computing (HPC) resources and can be utilized to explore the hierarchical control strategies of IBRs for the large-scale T&D hybrid system.
Optimal power management (OPM) is critical for large-scale battery energy storage systems. Today’s methods often require formidable computational effort due to the design based on centralized numerical optimization. Thus, this paper investigates computationally distributed OPM where the agents based on the cells communicate over a network to cooperatively solve the OPM problem. We propose an accelerated tracking alternating direction method of multipliers (ADMM) algorithm to solve the distributed OPM. The proposed algorithm embeds dynamic average consensus and Nesterov’s acceleration technique in the ADMM algorithm. Not only is the proposed algorithm fully distributed without a need for fusion or aggregating nodes, but it also accelerates convergence. The paper formulates the OPM in a model predictive control framework where it seeks to regulate the charging/discharging power of each battery cell to minimize the total power losses and promote balanced use of the constituent cells while complying with the safety constraints. The paper provides ample simulation results to demonstrate the effectiveness and advantages of the proposed distributed OPM in terms of computation and convergence.
A description is provided of the capabilities offered by the Spacelab design for doing research in space. The Spacelab flight vehicle consists of two basic elements including the habitable pressurized compartments and the unpressurized equipment mounting platforms. Spacelab services to payloads are considered, taking into account payload mass, electrical power and energy, heat rejection for Spacelab and payload, aspects of Spacelab data handling, and the extended flight capability. Attention is also given to the Spacelab structure, crew station and habitability, the electrical power distribution subsystem, the command and data management subsystem, the experiment computer operating system, the environmental control subsystem, the experiment vent assembly, the common payload support equipment, the instrument pointing subsystem, and details concerning the utilization of Spacelab.
The paper attempts to define and characterize a set of VLSI (very large scale integration) building-block circuits which can be used to combine existing microprocessors and memories into a wide variety of fault-tolerant computing systems. Such VLSI circuits would transform fault-tolerant computing into an off-the-shelf technology and enable its routine use for new applications. The self-checking computer module (SCCM) is the basic component out of which fault-tolerant computer systems are constructed. Several fault-tolerant configurations of SCCM are discussed, including the standby redundant uniprocessor, the voted/hybrid uniprocessor, and the distributed computer network.
The NASA Computer Graphics Pilot Project was undertaken to enhance the quality control, productivity and efficiency of mission support operations at the Goddard Operations Support Computing Facility. The Project evolved into a set of demonstration programs for graphics intensive simulated control room operations, particularly in connection with the complex space missions that began in the 1980s. Complex mission mean more data. Graphic displays are a means to reduce the probabilities of operator errors. Workstations were selected with 1024 x 768 pixel color displays controlled by a custom VLSI chip coupled to an MC68010 chip running UNIX within a shell that permits operations through the medium of mouse-accessed pulldown window menus. The distributed workstations run off a host NAS 8040 computer. Applications of the system for tracking spacecraft orbits and monitoring Shuttle payload handling illustrate the system capabilities, noting the built-in capabilities of shifting the point of view and rotating and zooming in on three-dimensional views of spacecraft.
Research concerning flight computers for use on interplanetary probes is reviewed. The history of these computers from the Viking mission to the present is outlined. The differences between ground commercial computers and computers for planetary exploration are listed. The development of a computer for the Mariner Mark II comet rendezvous asteroid flyby mission is described. Various aspects of recently developed computer systems are examined, including the Max real time, embedded computer, a hypercube distributed supercomputer, a SAR data processor, a processor for the High Resolution IR Imaging Spectrometer, and a robotic vision multiresolution pyramid machine for processsing images obtained by a Mars Rover.
Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc.