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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 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 OSTI2013

Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural net approach it is possible to reduce the rate counts used to unfold the neutron spectrum. To evaluate these codes a computer tool called Neutron Spectrometry and dosimetry computer tool was designed. The results obtained with this package are showed. The codes here mentioned are freely available upon request to the authors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Explorations of new selenites of the group IIIA and IVA metals

Systematic explorations of new phases in the Ga{sup III}/In{sup III}/Ge{sup IV}-Se{sup IV}-O systems by hydrothermal syntheses or solid-state reactions at high-temperature led to six new ternary compounds, namely, M{sub 2}Se{sub 2}O{sub 7} (M=Ga 1, In 2), M(OH)(SeO{sub 3}) (M=Ga 3, In 4), {alpha}-Ge(SeO{sub 3}){sub 2}5 and {beta}-Ge(SeO{sub 3}){sub 2}6. Ga{sub 2}Se{sub 2}O{sub 7}1 displays a 3D open framework composed of 2D gallium oxide layers being further bridged and capped by SeO{sub 3} groups. In{sub 2}Se{sub 2}O{sub 7}2 features a 3D indium oxide framework formed by corner- and edge- sharing InO{sub 6} octahedra with SeO{sub 3} groups attached on the cavities and the 8-member ring tunnels of the structure. The isostructural of M(OH)(SeO{sub 3}) (M=Ga 3, In 4) exhibit a 2D metal selenite layer composed of 1D edge-sharing MO{sub 6} octahedral chains that are interconnected by SeO{sub 3} groups. {alpha}-Ge(SeO{sub 3}){sub 2} (P2{sub 1}/n) 5 displays a 3D open framework with 1D 8-member ring tunnels along the a-axis while {beta}-Ge(SeO{sub 3}){sub 2} (Pa-3) 6 exhibits a condensed 3D network. - Graphical abstract: Highlights: Black-Right-Pointing-Pointer Up to now, selenites of the group IIIA and IVA metals are still rare. Black-Right-Pointing-Pointer Hydrothermal or solid state reactions yielded six new compounds in this system. Black-Right-Pointing-Pointer They are M{sub 2}Se{sub 2}O{sub 7} (M=Ga, In), M(OH)(SeO{sub 3}) (M=Ga, In), {alpha}-Ge(SeO{sub 3}){sub 2} and {beta}-Ge(SeO{sub 3}){sub 2}. Black-Right-Pointing-Pointer They exhibit four different 3D and one 2D structural types. Black-Right-Pointing-Pointer {alpha}-Ge(SeO{sub 3}){sub 2} and {beta}-Ge(SeO{sub 3}){sub 2} represent the first examples of germanium selenites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 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.

Potok, Thomas E↗
DOE OSTI2012

Li{sub 2}Sr{sub 4}B{sub 12}O{sub 23}: A new alkali and alkaline-earth metal mixed borate with [B{sub 10}O{sub 18}]{sup 6-} network and isolated [B{sub 2}O{sub 5}]{sup 4-} unit

A novel ternary lithium strontium borate Li{sub 2}Sr{sub 4}B{sub 12}O{sub 23} crystal with size up to 20 mm Multiplication-Sign 10 mm Multiplication-Sign 4 mm has been grown via the top-seeded solution growth method below 730 Degree-Sign C. Single-crystal XRD analyses showed that Li{sub 2}Sr{sub 4}B{sub 12}O{sub 23} crystallizes in the monoclinic space group P2{sub 1}/c with a=6.4664(4) A, b=8.4878(4) A, c=15.3337(8) A, {beta}=102.02(3) Degree-Sign , Z=2. The crystal structure is composed of [B{sub 10}O{sub 18}]{sup 6-} network and isolated [B{sub 2}O{sub 5}]{sup 4-} unit. The IR spectrum further confirmed the presence of both BO{sub 3} and BO{sub 4} groups. TG-DSC and Transmission spectrum were reported. Band structures and density of states were calculated. - Graphical abstract: A new phase, Li{sub 2}Sr{sub 4}B{sub 12}O{sub 23}, has been discovered in the ternary M{sub 2}O-M Prime O-B{sub 2}O{sub 3} (M=alkali-metal, M Prime =alkalineearth metal) system. The crystal structure consists of [B{sub 10}O{sub 18}]{sup 6-} network and isolated [B{sub 2}O{sub 5}]{sup 4-} unit. Highlights: Black-Right-Pointing-Pointer Li{sub 2}Sr{sub 4}B{sub 12}O{sub 23} is a a novel borate discovered in the M{sub 2}O-M Prime O-B{sub 2}O{sub 3} (M=alkali-metal, M Prime =alkaline-earth metal) system. Black-Right-Pointing-Pointer Li{sub 2}Sr{sub 4}B{sub 12}O{sub 23} crystal structure has a three-dimensional crystal structure with [B{sub 10}O{sub 18}]{sup 6-} network and isolated [B{sub 2}O{sub 5}]{sup 4-} unit. Black-Right-Pointing-Pointer Sr{sub 1} and Sr{sub 2} are located in two different channels constructed by {sup 3}{sub {infinity}}[B{sub 10}O{sub 18}] network.

36 MATERIALS SCIENCE↗
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.↗
DOE OSTI2012

Straddle Carrier Interface and Dispatching System

SCIDS is the Data Dispatching and Transfer Point (DDTP) component of a straddle carrier-based radiation detection system developed for the DOE Megaports Initiative for scanning shipping containers in transshipment ports. Its purpose is to communicate with a Radiation Detection Straddle Carrier (RDSC) developed by Detector Networks International, sending commands to the RDSC and receiving sensor data from the RDSC. Incoming sensor and status data from the RDSC is forwarded to a back-end data storage and display system that is external to SCIDS. SCIDS provides a graphical user interface for port operations personnel that displays location and status of the RDSC and status of each container in the port, and accepts commands from the operator directing the scanning operations of the RDSC.

Source record↗
DOE OSTI2016

Utilization of mixed ligands to construct diverse Ni(II)-coordination polymers based on terphenyl-2,2′,4,4′-tetracarboxylic acid and varied N-donor co-ligands

Three new coordination polymers, namely, {[Ni(H_2L)(bix)(H_2O)_2]·2h_2O}{sub n} (1), {[Ni(HL)(Hdpa)(H_2O)_2]·H_2O}{sub n} (2), {[Ni(L)_0_._5(bpp)(H_2O)]·H_2O}{sub n} (3) (H{sub 4}L=terphenyl-2,2′,4,4′-tetracarboxylic acid; bix=1,4-bis(imidazol-1-ylmethyl)benzene; dpa =4,4′-dipyridylamine; bpp=1,3-bis(4-pyridyl)propane), based on rigid H{sub 4}L ligand and different N-donor co-ligands, have been synthesized under hydrothermal conditions. Compound 1 features a 3D 4-connected 6{sup 6}-dia-type framework with H{sub 4}L ligand adopts a μ{sub 2}-bridging mode with two symmetry-related carboxylate groups in μ{sub 1}-η{sup 1}:η{sup 0} monodentate mode. Compound 2 displays a 1D [Ni(HL)(Hdpa)]{sub n} ribbon chains motif, in which the H{sub 4}L ligand adopts a μ{sub 2}-bridging mode with two carboxylate groups in μ{sub 1}-η{sup 1}:η{sup 1} and μ{sub 1}-η{sup 1}:η{sup 0} monodentate modes, while 3 possesses a (4,4)-connected 3D frameworks with bbf topology, with H{sub 4}L ligand displays a μ{sub 4}-bridging coordination mode. The H{sub 4}L ligand displays not only different deprotonated forms but also diverse coordination modes and conformations. The structural diversities among 1–3 have been carefully discussed, and the roles of N-donor co-ligands in the self-assembly of coordination polymers have been well documented. - Graphical abstract: Three nickel coordination polymers with different architectures based on mixed ligand system were synthesized and structurally characterized. Topology analyses indicate that 1 shows the 4-connected 6{sup 6}-dia net, 1D ribbon chains for 2 and 3D (4,4)-connected bbf network for 3. Display Omitted - Highlights: • Three Ni-based coordination polymers with distinct features have been prepared. • Compound 1 features a 3D 4-connected 66-dia-type framework, 2 displays a 1D [Ni(HL)(Hdpa)]{sub n} ribbon chains motif, while 3 possesses a (4,4)-connected 3D frameworks with bbf topology. • The “mixed ligand assembled” strategy is significant potential for network design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Low-dimensional compounds containing cyanido groups. XXVIII. Crystal structure, spectroscopic and magnetic properties of two copper(II) tetracyanidoplatinate complexes with 1,2-diaminopropane

Violet crystals of ([Cu(pn){sub 2}]{sub 2}[Pt(CN){sub 4}])[Pt(CN){sub 4}]·2H{sub 2}O (1, pn=1,2-diaminopropane) and blue crystals of [Cu(pn)Pt(CN){sub 4}]{sub n}·nH{sub 2}O (2) were prepared under hydrothermal conditions and characterized using elemental analysis, IR and UV–vis spectroscopy and by X-ray crystal structure analysis. Different number of ν(C≡N) absorption bands of these two compounds reflects their different structures. An X-ray crystal structure analysis has shown that complex 1 is of ionic character and is formed from trinuclear [Cu(pn){sub 2}–Pt(CN){sub 4}–Cu(pn){sub 2}]{sup 2+} complex cation and discrete [Pt(CN){sub 4}]{sup 2–} anion together with two molecules of crystal water. On the other hand, complex 2 is of polymeric character and is formed by 2D networks of [Cu(pn)Pt(CN){sub 4}]{sub n} composition and completed by n molecules of crystal water. Magnetic measurements show the presence of a weak antiferromagnetic exchange interaction in complex 1 (Θ=–0.2 K), while the magnetic susceptibility of complex 2 is well described by the model of uniform S=1/2 spin chain with exchange interaction J/k{sub B}=–1.64 K. - Graphical abstract: Two complexes of different structural types from the system Cu(II) – 1,2–diaminopropane – [Pt(CN){sub 4}]{sup 2–} have been isolated. These were characterized by IR and UV–VIS spectroscopy, X–ray crystal structure analysis together with the magnetic measurements. On one hand ([Cu(pn){sub 2}]{sub 2}[Pt(CN){sub 4}])[Pt(CN){sub 4}]∙2H{sub 2}O is of ionic character and is formed from trinuclear complex cation and discrete anion together with two molecules of crystal water. On the other hand, [Cu(pn)Pt(CN){sub 4}]{sub n}∙nH{sub 2}O is of polymeric character and is formed by 2D networks of [Cu(pn)Pt(CN){sub 4}]{sub n} composition and completed by n molecules of crystal water. - Highlights: • Two complexes of different compositions from one system have been isolated. • First complex is of ionic character and second one is of polymeric character. • Polymeric complex described as a spin chain in contrast to its bilayer structure.

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

GUI Control System for the Mu2e Electrostatic Septum High Voltage at Fermilab

The Mu2e Experiment has stringent beam structure requirements; namely, its proton bunches with a time structure of 1.7 $\mu$s in the Fermilab Delivery Ring. This beam structure will be delivered using the Fermilab 8-GeV Booster, the 8-GeV Recycler Ring, and the Delivery Ring. The 1.7-$\mu$s period of the Delivery Ring will generate the required beam structure by means of a third order resonant extraction system operating on a single circulating bunch. The electrostatic septum (ESS) for this system is particularly challenging, requiring mechanical precision in a ultra high vacuum of 1 x 10$^-8$ Torr to generate 100 kV across 15 mm. This paper describes a graphical user interface that has been developed to automate the conditioning and commissioning process for the electrostatic septa. It is based on an interface to the Fermilab ACNET system using the ACSys Python Data Pool Manager (DPM) Client produced and maintained by Fermilab Accelerator Controls. Network interfacing between data pool managers made by the application and ACNET devices introduce an inherent (approximately 1 s) latency in throughput of the readouts. This delay is utilized to process and graph incoming data events of devices crucial to conditioning of a electrostatic septum (ESS). 'Ramping' and 'Monitoring' modes adjust settings of the power supply based on internal logic to efficaciously increase and maintain the high voltage (HV) in the ESS, easing the voltage setting on incidence of sparking or other possibly damaging events. A timestamped log file is produced as the application runs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network

Advances in nuclear power technologies require enhanced capabilities for operator advice and autonomous control. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for structures, systems, and components (SSCs) of interest, for which the primary goal is to aid plant personnel in deducing the probabilistic performance status of the monitored SSCs and in detecting impending faults/failure. The task of conditional failure probability estimation is a bidirectional inference problem and shall be logically tackled by the Bayesian network (BN) approach. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers the capability of reasoning under uncertainty and graphical representation emulating the physical behavior of the target SSC. This paper provides a systematic overview of the BN technique and the software tools for handling implementation of BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgement can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. Our focus is upon failure-prone and risk-important balance of plant assets, especially cases having strong operator involvement. An exemplary case study on the failure of a motor-driven centrifugal pump is also conducted to demonstrate the usefulness and technical feasibility of the proposed artificial reasoning system using an expert system shell.

Zhao, Xingang↗