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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A graphical user interface platform to support power electronic converters and integrated systems

Power Electronic Systems have grown in size and complexity over the past years. Therefore, users require interfaces to properly configure, develop, and deploy these systems. This work presents a Graphical User Interface that dynamically adapts to the system and allows the user to control and monitor its operation. This GUI uses networked communication and open-source Python libraries to allow the user to interact with the system at its different hierarchical layers, as well as visualize the interconnection of the full system. This is demonstrated by showing the user interface applied to a megawatt class charging station that consists of various energy resources.

Dean, Ben↗
DOE OSTI2016

N-donor co-ligands driven two new Co(II)- coordination polymers with bi- and trinuclear units: Crystal structures, and magnetic properties

Two new Co(II) coordination polymers(CPs), namely [Co{sub 2}(bpe){sub 2}(Hbppc)]{sub n} (1) and [Co{sub 3}(μ{sub 3}-OH)(bppc)(bpm)(H{sub 2}O)]·3H{sub 2}O (2) (H{sub 5}bppc=biphenyl-2,4,6,3′,5′-pentacarboxylic acid, bpe=1,2-bis(4-pyridyl)ethene, bpm=bis(4-pyridyl)amine), have been obtained and characterized by elemental analysis, single-crystal X-ray diffraction, powder X-ray diffraction (PXRD), IR spectra and thermogravimetric analysis (TGA). 1 shows a binodal (4,6)-connected fsc net with a (4{sup 4}·6{sup 10}·8)(4{sup 4}·6{sup 2}) topology, while 2 shows a binodal (5,7)-connected 3D network based on trinuclear [Co{sub 3}(μ{sub 3}-OH)]{sup 5+} units with unusual (3.4{sup 6}.5{sup 2}.6)(3{sup 2}.4{sup 6}.5{sup 7}.6{sup 5}.7) topology. Variable-temperature magnetic susceptibility measurements reveals that complex 1 shows ferromagnetic interactions between the adjacent Co(II) ions, whereas 2 is a antiferromagnetic system. - Graphical abstract: Two new Co(II) coordination polymers with bi- and trinuclear units have been obtained. 1 shows a binodal (4,6)-connected fsc net with a (4{sup 4}·6{sup 10}·8)(4{sup 4}·6{sup 2}) topology and antiferromagnetic interactions between the adjacent Co(II) ions, while 2 is a binodal (5,7)-connected 3D network with unusual (3.4{sup 6}.5{sup 2}.6)(3{sup 2}.4{sup 6}.5{sup 7}.6{sup 5}.7) topology and a ferromagnetic system. - Highlights: • Two Co(II) coordination polymers with different multimetallic clusters as building units. • A (4,6)-connected fsc net and a (5,7)-connected 3D network. • A antiferromagnetic coupling for 1 and A ferromagnetic coupling for 2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

DC distribution↗
DOE OSTI2022

Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

Zhao, Junfeng↗
DOE OSTI2019

Syntgen: a system to generate temporal networks with user-specified topology

Abstract In the last few years, the study of temporal networks has progressed markedly. The evolution of clusters of nodes (or communities) is one of the major focus of these studies. However, the time dimension increases complexity, introducing new constructs and requiring novel and enhanced algorithms. In spite of recent improvements, the relative scarcity of timestamped representations of empiric networks, with known ground truth, hinders algorithm validation. A few approaches have been proposed to generate synthetic temporal networks that conform to static topological specifications while in general adopting an ad hoc approach to temporal evolution. We believe there is still a need for a principled synthetic network generator that conforms to problem domain topological specifications from a static as well as temporal perspective. Here, we present such a system. The unique attributes of our system include accepting arbitrary node degree and cluster size distributions and temporal evolution under user control, while supporting tunable joint distribution and temporal correlation of node degrees. Theoretical contributions include the analysis of conditions for graphic sequences of inter- and intracluster node degrees and cluster sizes and the development of a heuristic to search for the cluster membership of nodes that minimizes the shared information distance between clusterings. Our work shows that this system is capable of generating networks under user controlled topology with up to thousands of nodes and hundreds of clusters with strong topology adherence. Much larger networks are possible with relaxed requirements. The generated networks support algorithm validation as well as problem domain analysis.

Pereira, Luis Ramada↗
DOE OSTI2025

Entanglement swapping systems toward a quantum internet

We demonstrate conditional entanglement swapping, i.e. teleportation of entanglement, between time-bin qubits at the telecommunication wavelength of 1536.4 nm with high fidelity of 87%. Our system is deployable, utilizing modular, off-the-shelf, fiber-coupled, and electrically controlled components such as electro-optic modulators. It leverages the precise timing resolution of superconducting nanowire detectors, which are controlled and read out via a custom developed graphical user interface. The swapping process is described, interpreted, and guided using characteristic function-based analytical modeling that accounts for realistic imperfections. Our system supports quantum networking protocols, including source-independent quantum key distribution, with an estimated secret key rate of approximately 0.5 bits per sifted bit.

Davis, Samantha I. [Caltech] (ORCID:00000001999481↗
DOE OSTI2023

Risk-Informed Condition Evaluation of Solar-centered Energy Generation and Distribution Networks through Bayesian Learning and Inference

We develop a methodology based on Bayesian inference over Probabilistic Graphical Models (PGMs) to understand and quantify risk in solar-centered grids using targeted measurements and learned system behavior. Being non-prescriptive but, rather, able to infer system behavior and, ultimately, address risk queries from data, our machine learning-type paradigm is tailored for diverse topologies and threat scenarios often associated with distributed energy generation and photovoltaic distributed energy resources (PV-DERs) in particular. We describe algorithmic processes for: (i) learning the structure of PGMs that result from attack-prone PV-DER-proliferated distribution systems, (ii) quantifying cause-effect relationships, and (iii) evaluating risk queries based on diverse evidence. The contributions are illustrated on a residential grid subject to output impairment attacks on its PV-DER infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Neural Networks and Graph Algorithms with Next-Generation Processors

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.

Hamilton, Kathleen↗
DOE OSTI2016

Synthesis, structure and optical properties of two isotypic crystals, Na{sub 3}MO{sub 4}Cl (M=W, Mo)

Two isotypic compounds, Na{sub 3}MO{sub 4}Cl (M = W, Mo) have been obtained from the high temperature solution, and their structures were determined by single-crystal X-ray diffraction. Both of them crystallize in the space group P4/nmm of tetragonal system with the unit cells: a=7.5181(15), c=5.360(2) for Na{sub 3}WO{sub 4}Cl and a=7.4942(12), c=5.3409(18) for Na{sub 3}MoO{sub 4}Cl. The structure exhibits a 3D network built up by the ClNa{sub 6} groups, and the MO{sub 4} groups reside in the tunnels of the 3D network. The structural similarities and differences between Na{sub 3}MO{sub 4}Cl (M=W, Mo) and Sr{sub 3}MO{sub 4}F (M=Al, Ga) have been discussed. Meanwhile, detailed structure comparison analyses between Na{sub 3}MO{sub 4}Cl (M=W, Mo) and Na{sub 3}MO{sub 4}F (M=W, Mo) indicate that the different connection modes of ClNa{sub 6} and FNa{sub 6} make Na{sub 3}MO{sub 4}Cl and Na{sub 3}MO{sub 4}F crystallize in different structures. The IR spectra were used to verify the validity of the structure. The diffuse reflectance spectra show that the UV absorption edges are about 249 nm (4.99 eV) and 265 nm (4.69 eV) for Na{sub 3}WO{sub 4}Cl and Na{sub 3}MoO{sub 4}Cl, respectively. In addition, the first-principles theoretical studies are also carried out to aid the understanding of electronic structures and linear optical properties. - Graphical abstract: Two isotypic compounds, Na{sub 3}MO{sub 4}Cl (M=W, Mo) have been obtained from the high temperature solution. Both of them crystallize in the space group P4/nmm of tetragonal system. The structure exhibits a 3D network built up by the ClNa{sub 6} groups, and the MO{sub 4} groups reside in the tunnels of the 3D network. - Highlights: • Structure and properties of Na{sub 3}MO{sub 4}Cl (M=W, Mo) are reported for the first time. • They show a 3D network built by ClNa{sub 6}, and WO{sub 4} lies in the tunnels of the network. • IR spectra were used to verify the validity of the structure. • Band structures and density of states have been calculated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Investigation into the effect on structure of oxoanion doping in Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O

In this paper an investigation into the effect of transition metal ion and selenate/fluorophosphate doping on the structures of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O (M=transition metal) materials is reported. In agreement with previous reports, the monoclinic (Kröhnkite) structure is adopted for M=Mn, Fe, Co, Cu, while for the smallest first row divalent transition metal ion, M=Ni, the triclinic (Fairfieldite structure) is adopted. On selenate doping there is a changeover in structure from monoclinic to triclinic for M=Fe, Co, Cu, with the larger Fe{sup 2+} system requiring the highest level of selenate to complete the changeover. Thus the results suggest that the relative stability of the two structure types is influenced by the relative size of the transition metal: oxoanion group, with the triclinic structure favoured for small transition metals/large oxoanions. The successful synthesis of fluorophosphate doped samples, Na{sub 2}M(SO{sub 4}){sub 2−x}(PO{sub 3}F){sub x}·2H{sub 2}O was also obtained for M=Fe, Co, Cu, with the results showing a changeover in structure from monoclinic to triclinic for M=Co, Cu for very low levels (x=0.1) of fluorophosphate. In the case of M=Fe, the successful synthesis of fluorophosphates samples was achieved for x≤0.3, although no change in cell symmetry was observed. Rather in this particular case, the X-ray diffraction patterns showed evidence for selective peak broadening, attributed to local disorder as a result of the fluorophosphate group disrupting the H-bonding network. Overall the work highlights how isovalent doping can be exploited to alter the structures of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O systems. - Graphical abstract: Partial substitution of sulfate in Na{sub 2}M(SO{sub 4}){sub 2}0.2H{sub 2}O (M=Co, Cu) by selenate or fluorophosphate leads to a structural change from the monoclinic Kröhnkite to the triclinic Fairfieldite structure. - Highlights: • The successful synthesis of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O (M=transition metal) phases doped with selenate and fluorophosphate. • A change in structure is observed on selenate doping for M=Fe, Co, Cu. • This change in structure is also observed on fluorophosphate doping for M=Co, Cu. • The work highlights how isovalent doping can be exploited to alter the structures of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Prototype Distributed Ledger Technology of UF 6 Cylinder Tracking in Ethereum

We have created a demonstration permissioned Distributed Ledger Technology (DLT) datastore for the UF 6 cylinder tracking safeguards use-case utilizing the Ethereum DLT framework and using Solidity for smart contract code. Our demonstration creates a simulated dataset representing tracking of 75,000 UF 6 cylinders across 11 example nuclear facilities worldwide. Our DLT system allows for easy input and reading of shipping and receiving data, including a Graphical User Interface (GUI). Sandia’s Emulytics capability was leveraged to help create the DLT node network and assess performance. We find that our DLT prototype can easily handle to ~150,000 UF 6 cylinder shipments per year worldwide, without any excessive computational or storage burden on the IAEA or Member States. Next steps could include a demonstration to the IAEA and potentially demonstrating integration with TradeLens, a DLT in use by a consortium of international shipping companies representing over half of world shipping trade.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Machine Learning

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Anomaly detection↗
DOE OSTI2016

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power.In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation.The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Benchmarking Operators in Deep Neural Networks for Improving Performance Portability of SYCL

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this paper, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, use of local memory, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.

Jin, Zheming [ORNL] (ORCID:000000027197780X)↗
DOE OSTI2024

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this work, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, shared local memory accesses, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Situating Green Infrastructure in Context: A Framework for Adaptive Socio‐Hydrology in Cities

Abstract Management of urban hydrologic processes using green infrastructure (GI) has largely focused on storm water management. Thus, design and implementation of GI usually rely on physical site characteristics and local rainfall patterns, and do not typically account for human or social dimensions. This traditional approach leads to highly centralized storm water management in a disconnected urban landscape and can deemphasize additional benefits that GI offers, such as increased property value, greenspace aesthetics, heat island amelioration, carbon sequestration, and habitat for biodiversity. We propose the Framework for Adaptive Socio‐Hydrology (FrASH) in which GI planning and implementation moves from a purely hydrology‐driven perspective to an integrated sociohydrological approach. This allows for an iterative, multifaceted decision‐making process that would enable a network of stakeholders to collaboratively set a dynamic, context‐guided project plan for the installation of GI, rather than a “one‐size‐fits‐all” installation. We explain how different sectors (e.g., governance, nongovernmental organizations, communities, academia, and industry) can create a connected network of organizations that work toward a common goal. Through a graphical Chambered Nautilus model, FrASH is experimentally applied to contrasting GI case studies and shows that this multistakeholder, connected, decentralized network with a coevolving decision‐making project plan results in enhanced multifunctionality, potentially allowing for the management of resilience in urban systems at multiple scales.

Schifman, L. A.↗
DOE OSTI2021

GPU remote communication with triggered operations

Methods, devices, and systems for transmitting data over a computer communications network are disclosed. A queue of communications commands can be pre-generated using a central processing unit (CPU) and stored in a device memory of a network interface controller (NIC). Thereafter, if a graphics processing unit (GPU) has data to communicate to a remote GPU, it can store the data in a send buffer, where the location in the buffer is pointed to by a pre-generated command. The GPU can then signal to the interface device that the data is ready, triggering execution of the pre-generated command to send the data.

LeBeane, Michael W.↗