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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Local Phasor-Based Control of DER Inverters for Voltage Regulation on Distribution Feeders

We introduce a new control paradigm termed Phasor-Based Control (PBC) to coordinate Distributed Energy Resources (DER) for improved voltage regulation and other objectives. PBC is a multi-layer control framework where the state of the distribution grid is represented by voltage phasors, and Phasor Measurement Units (PMUs) measure the state at critical points in the distribution grid. In the PBC paradigm, a supervisory controller (S-PBC) sets phasor targets which local controllers track. In this paper, we propose a Proportional Integral (PI) implementation for the local control layer (LPBC), to regulate real and reactive power output of distributed inverters to track a voltage phasor target computed by the supervisory layer. We tune the PI controller gains offline with a genetic algorithm that yields better performance than the Ziegler-Nichols method. We benchmark the proposed L-PBC controller against droop volt-var control (DVVC) and observe improved voltage regulation in simulation on the IEEE 13 node unbalanced test feeder (IEEE 13NF). Furthermore, by means of numerical simulation we observe the response of the L-PBC controllers to small and large grid disturbances. Simulations on larger feeders, IEEE 123-node and 647-node, demonstrate challenges with tuning controllers in setups with different amounts of power-voltage coupling.

42 ENGINEERING↗

NASA NTRS2022

VULCAN-CFD User Manual: Ver. 7.2.0

VULCAN-CFD offers a comprehensive set of capabilities to enable the simulation of continuum flowfields from subsonic to hypersonic conditions. The governing equations that are employed include allowances for both chemical and thermal nonequilibrium processes, coupled with a wide variety of turbulence models for both Reynolds-averaged and large eddy simulations. The software package can simulate two-dimensional, axisymmetric, or three-dimensional problems on structured multiblock meshes or fully unstructured meshes. A parabolic (i.e., space-marching) treatment can also be used for any subset of a structured mesh that can accommodate this solution strategy. The flow solver provides a significant level of geometric flexibility for structured grid simulations by allowing for arbitrary face-to-face C(0) continuous and non-C(0) continuous block interface connectivities. The unstructured grid paradigm allows for mixed element unstructured meshes that contain any combination of tetrahedral, prismatic, pyramidal, and hexahedral cell elements. The flow solver is also fully parallelized using MPI (Message Passing Interface) libraries in a data-parallel fashion, allowing for efficient simulations on modern High Performance Computing (HPC) systems. This document provides information related to the installation and execution of the VULCAN-CFD software package. A detailed description of the physical and numerical models available in the software are provided in the VULCAN-CFD Theory Manual.

VULCAN-CFD User Manual↗

DOE OSTI2022

Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data

We propose a novel statistical inference paradigm for zero-inflated multiway count data that dispenses with the need to distinguish between true and false zero counts. Our approach ignores all zero entries and applies zero-truncated Poisson regression on the positive counts. Inference is accomplished via tensor completion that imposes low-rank structure on the Poisson parameter space. Our main result shows that an $\textit{N}$-way rank-R parametric tensor 𝓜 ϵ (0, ∞) $I$Χ∙∙∙Χ$I$ generating Poisson observations can be accurately estimated from approximately $IR^2 \text{log}^2_2(I)$ non-zero counts for a nonnegative canonical polyadic decomposition. Several numerical experiments are presented demonstrating that our zero-truncated paradigm is comparable to the ideal scenario where the locations of false zero counts are known $\textit{a priori}$.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Dynamic and reversible transformations of subnanometre-sized palladium on ceria for efficient methane removal

Reversibly adjusting the active structures of supported metal catalysts in response to dynamic working conditions has long been pursued. Here we report the reaction-environment-modulated transformations of subnanometre-sized Pd on CeO 2 for efficient methane removal, leveraging the reaction environments at different stages of automotive exhaust aftertreatment. During the cold start of vehicles, inactive Pd 1 single atoms are readily transformed into PdO $x$ subnanometre clusters by CO even at room temperature with excess O 2 , resulting in boosted low-temperature CH 4 oxidation. At elevated temperatures, dispersion of PdO $x$ cluster into Pd 1 against metal sintering renders outstanding hydrothermal stability to the catalyst, to be activated during the next vehicle start. Combined experimental and computational studies elucidate the dynamically evolved Pd speciation on CeO 2 at an atomic level. In conclusion, modulating the reversible nature of supported metals helps overcome the long-existing trade-off between low-temperature activity and high-temperature stability, also providing a new paradigm for designing intelligent catalysts that brings single-atom/cluster catalysts closer to real applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Complex concentrated alloys and chemical short-range ordering

Abstract Complex concentrated alloys (CCAs) are materials comprising three or more elements in similar proportions and possessing structural but no chemical long-range order. Fascination with CCAs has grown over the last 20 years and to date, CCAs have opened a new materials design paradigm and horizon for discovery of materials to meet the demands of applications in aggressive environments. Understanding the fundamental mechanisms controlling their response, however, is challenging due to the chemical and structural variations that wildly fluctuate over fine atomic and nanoscales. This issue focuses on the experimental, computational, and theoretical investigations that aim to uncover phenomena and processes determining the structure, kinetics, mechanics, or deformation mechanisms in CCAs at the atomic scale. At the atomic scale at which they operate, chemical short-range ordering can be influential. This issue further addresses the capabilities, as well as the debatable need, to characterize, predict, and relate short-range ordering to material performance. Collectively, the articles in this issue highlight the insights, understanding, and experimental and computational tools that attempt to create property-tunable CCAs “from the atom up” by treating short-range ordering and engineering atomic-scale mechanisms. Graphical abstract

36 MATERIALS SCIENCE↗

NASA NTRS1991

Systems engineering studies of on-orbit assembly operation

While the practice of construction has a long history, the underlying theory of construction is relatively young. Very little has been documented as to techniques of logistic support, construction planning, construction scheduling, construction testing, and inspection. The lack of 'systems approaches' to construction processes is certainly one of the most serious roadblocks to the construction of space structures. System engineering research efforts at CSC are aimed at developing concepts and tools which contribute to a systems theory of space construction. The research is also aimed at providing means for trade-offs of design parameters for other research areas in CSC. Systems engineering activity at CSC has divided space construction into the areas of orbital assembly, lunar base construction, interplanetary transport vehicle construction, and Mars base construction. A brief summary of recent results is given. Several models for 'launch-on-time' were developed. Launch-on-time is a critical concept to the assembly of such Earth-orbiting structures as the Space Station Freedom, and to planetary orbiters such as the Mars transfer vehicle. CSC has developed a launch vehicle selection model which uses linear programming to find optimal combinations of launch vehicles of various sizes (Atlas, Titan, Shuttles, HLLV's) to support SEI missions. Recently, the Center developed a cost trade-off model for studying on orbit assembly logistics. With this model it was determined that the most effective size of the HLLV would be in the range of 120 to 200 metric tons to LEO, which is consistent with the choices of General Stafford's Synthesis Group Report. A second-generation Dynamic Construction Activities Model ('DYCAM') process model has been under development, based on our past results in interruptability and our initial DYCAM model. This second-generation model is built on the paradigm of knowledge-based expert systems. It is aimed at providing answers to two questions: (1) what are some necessary or sufficient conditions for judging conceptual designs of spacecraft?, and (2) can a methodology be formulated such that these conditions may be used to provide computer-aided tools for evaluating conceptual designs and planning for space assembly sequences? Early simulation results indicate that the DYCAM model has a clear ability to emulate and simulate human orbital construction processes.

Morgenthaler, George W.↗

NASA NTRS2001

Aspect-Oriented Programming

Computer science has experienced an evolution in programming languages and systems from the crude assembly and machine codes of the earliest computers through concepts such as formula translation, procedural programming, structured programming, functional programming, logic programming, and programming with abstract data types. Each of these steps in programming technology has advanced our ability to achieve clear separation of concerns at the source code level. Currently, the dominant programming paradigm is object-oriented programming - the idea that one builds a software system by decomposing a problem into objects and then writing the code of those objects. Such objects abstract together behavior and data into a single conceptual and physical entity. Object-orientation is reflected in the entire spectrum of current software development methodologies and tools - we have OO methodologies, analysis and design tools, and OO programming languages. Writing complex applications such as graphical user interfaces, operating systems, and distributed applications while maintaining comprehensible source code has been made possible with OOP. Success at developing simpler systems leads to aspirations for greater complexity. Object orientation is a clever idea, but has certain limitations. We are now seeing that many requirements do not decompose neatly into behavior centered on a single locus. Object technology has difficulty localizing concerns invoking global constraints and pandemic behaviors, appropriately segregating concerns, and applying domain-specific knowledge. Post-object programming (POP) mechanisms that look to increase the expressiveness of the OO paradigm are a fertile arena for current research. Examples of POP technologies include domain-specific languages, generative programming, generic programming, constraint languages, reflection and metaprogramming, feature-oriented development, views/viewpoints, and asynchronous message brokering. (Czarneclu and Eisenecker s book includes a good survey of many of these technologies).

Elrad, Tzilla↗

NASA NTRS2011

Translational Vestibulo-Ocular Reflex and Motion Perception During Interaural Linear Acceleration: Comparison of Different Motion Paradigms

The neural mechanisms to resolve ambiguous tilt-translation motion have been hypothesized to be different for motion perception and eye movements. Previous studies have demonstrated differences in ocular and perceptual responses using a variety of motion paradigms, including Off-Vertical Axis Rotation (OVAR), Variable Radius Centrifugation (VRC), translation along a linear track, and tilt about an Earth-horizontal axis. While the linear acceleration across these motion paradigms is presumably equivalent, there are important differences in semicircular canal cues. The purpose of this study was to compare translation motion perception and horizontal slow phase velocity to quantify consistencies, or lack thereof, across four different motion paradigms. Twelve healthy subjects were exposed to sinusoidal interaural linear acceleration between 0.01 and 0.6 Hz at 1.7 m/s/s (equivalent to 10 tilt) using OVAR, VRC, roll tilt, and lateral translation. During each trial, subjects verbally reported the amount of perceived peak-to-peak lateral translation and indicated the direction of motion with a joystick. Binocular eye movements were recorded using video-oculography. In general, the gain of translation perception (ratio of reported linear displacement to equivalent linear stimulus displacement) increased with stimulus frequency, while the phase did not significantly vary. However, translation perception was more pronounced during both VRC and lateral translation involving actual translation, whereas perceptions were less consistent and more variable during OVAR and roll tilt which did not involve actual translation. For each motion paradigm, horizontal eye movements were negligible at low frequencies and showed phase lead relative to the linear stimulus. At higher frequencies, the gain of the eye movements increased and became more inphase with the acceleration stimulus. While these results are consistent with the hypothesis that the neural computational strategies for motion perception and eye movements differ, they also indicate that the specific motion platform employed can have a significant effect on both the amplitude and phase of each.

Beaton, K. H.↗

DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗

DOE OSTI2026

Photoenzymatic Csp 3 –Csp 3 bond formation via enzyme-templated radical–radical coupling

Cross-couplings are essential reactions in modern chemical synthesis, enabling the rapid construction of complex molecules from simple precursors. Transition metal catalysts are prized for these transformations because their reactivity and selectivity can be tuned via judicious selection of the metal and ligand. Although enzymes offer analogous opportunities for tuning via protein engineering, their application to cross-coupling remains limited, as nature relies on alternative paradigms for building molecular complexity. Here, we report the cross-coupling of alkyl halides and benzylic carboxylic acids using an engineered flavin-dependent lactate monooxygenase—a photoenzyme. The enzyme achieves this feat by exploiting the redox versatility of the flavin cofactor. Stoichiometric experiments, ultrafast spectroscopy, and computational studies support a mechanism in which photoexcited flavin quinone initiates the reaction via oxidative decarboxylation to generate a benzylic radical. The resulting flavin semiquinone can reduce the alkyl halide to form a second organic radical within the protein active site, which rapidly engages in C(sp 3 )–C(sp 3 ) bond formation. A variant was engineered to control the stereochemical outcome of this radical–radical coupling event, highlighting the ability of the protein to alter the energetic barrier for a mechanistic step that is traditionally understood to be near barrierless. This work demonstrates that the scope for nonnative reaction mechanisms in biocatalysis far exceeds previously established bounds and has potential to solve a variety of reactivity challenges in cross-coupling chemistry.

biocatalysis↗

DOE OSTI2021

A Case Study on Pathogen Transport, Deposition, Evaporation and Transmission: Linking High-Fidelity Computational Fluid Dynamics Simulations to Probability of Infection

A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Engineering Dynamically Decoupled Quantum Simulations with Trapped Ions

An external drive can improve the coherence of a quantum many-body system by averaging out noise sources. It can also be used to realize models that are inaccessible in the static limit, through Floquet Hamiltonian engineering. The full possibilities for combining these tools remain unexplored. We develop the requirements needed for a pulse sequence to decouple a quantum many-body system from an external field without altering the intended dynamics. Demonstrating this technique experimentally in an ion-trap platform, we show that it can provide a large improvement to coherence in real-world applications. Finally, we engineer an approximate quantum simulation of the Haldane-Shastry model, an exactly solvable paradigm for long-range interacting spins. Our results expand and unify the quantum simulation toolbox.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

OpenCGRA: An Open-Source Unified Framework for Modeling,Testing, and Evaluating CGRAs

Coarse-grained reconfigurable arrays (CGRAs),loosely defined as arrays of functional units (e.g, adder, sub-tractor, multiplier, divider, or larger multi-operation units, butsmaller than a general-purpose core) interconnected through aNetwork-on-Chip, provide higher flexibility than domain-specificASIC accelerators while offering increased hardware efficiencywith respect to fine-grained reconfigurable devices, such as FieldProgrammable Gate Arrays (FPGAs). The fast evolving fieldsof machine learning and edge computing, which are seeing acontinuous flow of novel algorithms and larger models, makeCGRAs ideal target architectures to allow domain specializationwithout loosing too much generality. They also generally offerquicker and more effective reconfigurability than FPGAs, po-tentially allowing adaptation during actual algorithm execution,and implement a dataflow programming paradigm that adaptswell to these emerging workloads. Designing and generating aCGRA, however, still requires to define the type and number ofthe specific functional units, implement their interconnect andthe network topology, and perform its simulation and validation,given a variety of workloads of interest.In this paper, we propose OpenCGRA, a Python-based unifiedframework that integrates generation, modeling, testing and eval-uation for CGRAs. OpenCGRA is the first open-source integratedframework able to support the full top-to-bottom design flow forspecializing and implementing CGRAs: modeling at different ab-straction levels (functional level, cycle level, register-transfer level),generation, simulation, testing at different granularities (unit test-ing, integration testing, property-based testing), and characteriza-tion (area, power, and timing). OpenCGRAs will be made availableon GitHub.

CGRA, synthesis↗

NASA NTRS2004

Employing Nested OpenMP for the Parallelization of Multi-Zone Computational Fluid Dynamics Applications

In this paper we describe the parallelization of the multi-zone code versions of the NAS Parallel Benchmarks employing multi-level OpenMP parallelism. For our study we use the NanosCompiler, which supports nesting of OpenMP directives and provides clauses to control the grouping of threads, load balancing, and synchronization. We report the benchmark results, compare the timings with those of different hybrid parallelization paradigms and discuss OpenMP implementation issues which effect the performance of multi-level parallel applications.

Ayguade, Eduard↗

NASA NTRS1992

Automatic data partitioning on distributed memory multicomputers

Distributed-memory parallel computers are increasingly being used to provide high levels of performance for scientific applications. Unfortunately, such machines are not very easy to program. A number of research efforts seek to alleviate this problem by developing compilers that take over the task of generating communication. The communication overheads and the extent of parallelism exploited in the resulting target program are determined largely by the manner in which data is partitioned across different processors of the machine. Most of the compilers provide no assistance to the programmer in the crucial task of determining a good data partitioning scheme. A novel approach is presented, the constraints-based approach, to the problem of automatic data partitioning for numeric programs. In this approach, the compiler identifies some desirable requirements on the distribution of various arrays being referenced in each statement, based on performance considerations. These desirable requirements are referred to as constraints. For each constraint, the compiler determines a quality measure that captures its importance with respect to the performance of the program. The quality measure is obtained through static performance estimation, without actually generating the target data-parallel program with explicit communication. Each data distribution decision is taken by combining all the relevant constraints. The compiler attempts to resolve any conflicts between constraints such that the overall execution time of the parallel program is minimized. This approach has been implemented as part of a compiler called Paradigm, that accepts Fortran 77 programs, and specifies the partitioning scheme to be used for each array in the program. We have obtained results on some programs taken from the Linpack and Eispack libraries, and the Perfect Benchmarks. These results are quite promising, and demonstrate the feasibility of automatic data partitioning for a significant class of scientific application programs with regular computations.

Gupta, Manish↗

NASA NTRS1993

Analog Processor To Solve Optimization Problems

Proposed analog processor solves "traveling-salesman" problem, considered paradigm of global-optimization problems involving routing or allocation of resources. Includes electronic neural network and auxiliary circuitry based partly on concepts described in "Neural-Network Processor Would Allocate Resources" (NPO-17781) and "Neural Network Solves 'Traveling-Salesman' Problem" (NPO-17807). Processor based on highly parallel computing solves problem in significantly less time.

Duong, Tuan A.↗

NASA NTRS2022

Detection and Mitigation of Transient Instabilities in Deployable Booms

Deployable space structures offer versatile uses in many space applications, as they can create very large space structures from a single space launch. However, they usually present important dynamical challenges during the critical deployment phase. In this paper, we focus on the dynamical behavior of deployable booms and the important challenge of avoiding collisions between boom’s structural elements or with other parts of the spacecraft, that can potentially have serious consequences. We define this kind of collisions as “transient instabilities” in deployable booms. A new paradigm to tackle these issues is presented in this paper, using techniques from multi-agent/swarm literature like transient instability prevention methods that avoid inter-agent collisions. We have adapted these techniques to deployable booms to guarantee safe deployment of booms and avoid collisions. We also present computational simulations of boom deployment in order to validate our techniques and demonstrate its use for future space missions.

Balas, Mark↗

DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗