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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TermGenie – a web-application for pattern-based ontology class generation

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 new classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. Lastly, TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

New Two-Cylinder Prototype Demonstration and Concept Design of a Next Generation Class 3-6 Opposed Piston Engine (Final Report)

The objective of the project is to research, develop, and test an Opposed Piston (OP) engine prototype capable of ≥10% fuel economy improvement over a 2015 baseline diesel engine while meeting prevailing emissions standards and include a cost analysis of the proposed technologies that validates an operating cost payback of less than 2 years. Results: In direct comparison with the baseline Isuzu model, the OP engine exhibited a noteworthy average fuel economy improvement exceeding 12% across five distinct driving cycles, including the Interstate cycle, Congested interstate cycle, GM-city cycle, HWFET cycle, and FTP cycle, with Interstate cycle benefited the most.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

A first class constraint generates not a gauge transformation, but a bad physical change: The case of electromagnetism

In Dirac–Bergmann constrained dynamics, a first-class constraint typically does not alone generate a gauge transformation. By direct calculation it is found that each first-class constraint in Maxwell’s theory generates a change in the electric field E{sup →} by an arbitrary gradient, spoiling Gauss’s law. The secondary first-class constraint p{sup i},{sub i}=0 still holds, but being a function of derivatives of momenta (mere auxiliary fields), it is not directly about the observable electric field (a function of derivatives of A{sub μ}), which couples to charge. Only a special combination of the two first-class constraints, the Anderson–Bergmann–Castellani gauge generator G, leaves E{sup →} unchanged. Likewise only that combination leaves the canonical action invariant—an argument independent of observables. If one uses a first-class constraint to generate instead a canonical transformation, one partly strips the canonical coordinates of physical meaning as electromagnetic potentials, vindicating the Anderson–Bergmann Lagrangian orientation of interesting canonical transformations. The need to keep gauge-invariant the relation q-dot −(δH)/(δp) =−E{sub i}−p{sup i}=0 supports using the gauge generator and primary Hamiltonian rather than the separate first-class constraints and the extended Hamiltonian. Partly paralleling Pons’s criticism, it is shown that Dirac’s proof that a first-class primary constraint generates a gauge transformation, by comparing evolutions from identical initial data, cancels out and hence fails to detect the alterations made to the initial state. It also neglects the arbitrary coordinates multiplying the secondary constraints inside the canonical Hamiltonian. Thus the gauge-generating property has been ascribed to the primaries alone, not the primary–secondary team G. Hence the Dirac conjecture about secondary first-class constraints as generating gauge transformations rests upon a false presupposition about primary first-class constraints. Clarity about Hamiltonian electromagnetism will be useful for an analogous treatment of GR. - Highlights: • A first-class constraint changes the electric field E, spoiling Gauss’s law. • A first-class constraint does not leave the action invariant or preserve q,0−dH/dp. • The gauge generator preserves E,q,0−dH/dp, and the canonical action. • The error in proofs that first-class primaries generating gauge is shown. • Dirac’s conjecture about secondary first-class constraints is blocked.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Gigawatt-class radiation generated by a Ka-band overmoded Cherenkov-type high power millimeter wave generator

Particle simulation and experimental results are presented about a Ka-band overmoded Cherenkov-type high power millimeter wave generator in this paper. The relativistic electron beam with peak current of 8.4 kA was generated by a pulsed power accelerator working at the voltage of 625 kV, which was guided by an axial magnetic field of 1.05 T and transported through the beam-wave interaction structures. After careful calibration, the microwave power radiated in the far field was as high as about 500 MW, with a frequency of 32.1 GHz and a pulse width of 20 ns. The radiation mode was well controlled to be TM{sub 0n} mode.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

GridSTIX

SF-25-112 Grid-STIX is a comprehensive extension of the STIX (Structured Threat Information Expression) 2.1 ontology specifically designed for electrical grid cybersecurity applications. This ontology provides a standardized, machine-readable framework for modeling grid assets, operational technology devices, threats, vulnerabilities, supply chain risks, and security relationships in electrical power systems. ## Key Features - **Comprehensive Grid Coverage**: Physical assets, OT devices, grid components, sensors, and energy storage systems - **Zero Trust Architecture**: Policy decision points, enforcement points, trust brokers, and continuous monitoring - **AMI Infrastructure**: Advanced metering networks, head-end systems, mesh gateways, and MDM systems - **Advanced Security Modeling**: Attack patterns, vulnerabilities, mitigations, and supply chain risks - **Critical Grid Relationships**: Power flow, protection, control, and synchronization relationships - **Supply Chain Security**: Supplier modeling, country of origin tracking, and risk assessment - **Protocol Support**: DNP3, Modbus, IEC 61850, IEC 60870-5-104, OPC-UA, and IEEE standards - **Python Code Generation**: Automated STIX-compliant Python class generation from ontologies - **Interactive Visualization**: Enhanced HTML network graphs with grid-specific categorization - **STIX 2.1 Compliance**: Full compatibility with STIX threat intelligence ecosystem

Blakely, Benjamin [Argonne National Laboratory (AN↗
DOE OSTI2018

Performance Excellence Lean Six Sigma

Lean Six Sigma (LSS) organization at Y-12 sponsors the deployment of Performance Enterprise System (PES) and provides continuous improvement facilitation and training for the Y-12 site. Six- Question Survey was issued to 100 Y-12 employees at random, with the option to report one’s organization. Goals included: To gain an insight of Y-12 employees knowledge of PES & the Lean Six Sigma group • Generate employee desire to take PES class • Generate employee desire to perform continuous improvement.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Collectively loading programs in a multiple program multiple data environment

Techniques are disclosed for loading programs efficiently in a parallel computing system. In one embodiment, nodes of the parallel computing system receive a load description file which indicates, for each program of a multiple program multiple data (MPMD) job, nodes which are to load the program. The nodes determine, using collective operations, a total number of programs to load and a number of programs to load in parallel. The nodes further generate a class route for each program to be loaded in parallel, where the class route generated for a particular program includes only those nodes on which the program needs to be loaded. For each class route, a node is selected using a collective operation to be a load leader which accesses a file system to load the program associated with a class route and broadcasts the program via the class route to other nodes which require the program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Collectively loading programs in a multiple program multiple data environment

Techniques are disclosed for loading programs efficiently in a parallel computing system. In one embodiment, nodes of the parallel computing system receive a load description file which indicates, for each program of a multiple program multiple data (MPMD) job, nodes which are to load the program. The nodes determine, using collective operations, a total number of programs to load and a number of programs to load in parallel. The nodes further generate a class route for each program to be loaded in parallel, where the class route generated for a particular program includes only those nodes on which the program needs to be loaded. For each class route, a node is selected using a collective operation to be a load leader which accesses a file system to load the program associated with a class route and broadcasts the program via the class route to other nodes which require the program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Factorization of generalized γ-generating matrices

The class of γ-generating matrices and its subclasses of regular and singular γ-generating matrices were introduced by D. Z. Arov in [8], where it was shown that every γ-generating matrix admits an essentially unique regular–singular factorization. The class of generalized γ-generating matrices was introduced in [14, 20]. In the present paper, subclasses of singular and regular generalized –generating matrices are introduced and studied. As the main result, a theorem of existence of the regular–singular factorization for a rational generalized γ-generating matrix is proved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Deformation Behavior in Neutron Irradiated Generation II FeCrAl Alloys for Accident Tolerant Fuel Cladding

Preliminary work has been completed to evaluate the deformation behavior of the Generation II FeCrAl alloy class. The Generation II alloy, C35M, was neutron irradiated within the High Flux Isotop e Reactor followed by post-irradiation examination to evaluate the mechanical properties, irradiated microstructure, deformation modes, and deformation microstructure. Complementary characterization techniques were used in this evaluation. It was found tha t aft er irradiation to1.8 dpa at 364 °C that the C35M specimen failed in a ductile manner. The observed deformation microstructure showed a high density of line dislocations, dislocation networks, and dislocation pile-ups. The observed performance and stru cture s are consistent with literature studies on high-Cr FeCr alloys that were thermally aged at 500°C. The observed results suggest that Generation II FeCrAl alloys retain ductility under low-dose, prototypical LWR irradiation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2014

ePix: a class of architectures for second generation LCLS cameras

ePix is a novel class of ASIC architectures, based on a common platform, optimized to build modular scalable detectors for LCLS. The platform architecture is composed of a random access analog matrix of pixel with global shutter, fast parallel column readout, and dedicated sigma-delta analog-to-digital converters per column. It also implements a dedicated control interface and all the required support electronics to perform configuration, calibration and readout of the matrix. Based on this platform a class of front-end ASICs and several camera modules, meeting different requirements, can be developed by designing specific pixel architectures. This approach reduces development time and expands the possibility of integration of detector modules with different size, shape or functionality in the same camera. The ePix platform is currently under development together with the first two integrating pixel architectures: ePix100 dedicated to ultra low noise applications and ePix10k for high dynamic range applications.

Dragone, A.↗
DOE OSTI2024

Comparison of point cloud and image-based models for calorimeter fast simulation

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Furthermore, point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Learning generative neural networks with physics knowledge

Deep generative neural networks have enabled modeling complex distributions, but incorporating physics knowledge into the neural networks is still challenging and is at the core of current physics-based machine learning research. To this end, we propose a physics generative neural network (PhysGNN), a new class of generative neural networks for learning unknown distributions in a physical system described by partial differential equations (PDE). PhysGNN couples PDE systems with generative neural networks. It is a fully differentiable model that allows back-propagation of gradients through both numerical PDE solvers and generative neural networks, and is trained by minimizing the discrete Wasserstein distance between generated and observed probability distributions of the PDE outputs using the stochastic gradient descent method. Moreover, PhysGNN does not require adversarial training like standard generative neural networks, which offers better stability than adversarial training. We show that PhysGNN can learn complex distributions in stochastic inverse problems, where conventional methods such as maximum likelihood estimation and momentum matching methods may be inapplicable when little knowledge is known about the form of unknown distributions or the physical model is too complex. Furthermore, our method allows physics-based generative neural network training for learning complex distributions in the context of differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A compact, high resolution energy, and emittance diagnostic for electron beams using active plasma lenses

High-resolution (sub-%), single-shot energy distribution, and emittance measurements of GeV-class electron beams generated by laser plasma accelerators (LPAs) have been enabled through the use of an active plasma lens combined with relatively short dipole magnets. This setup, with only 60 cm between the LPA source and the final diagnostic screen, was facilitated through the use of a replenishable ∼20–40 nm thick liquid crystal plasma mirror to separate remnant laser radiation from the electron beam without emittance degradation. As LPAs capable of generating GeV-class electron beams at cm-scale distances become increasingly ubiquitous, there is a need to supplement these compact accelerators with equally compact diagnostics.

Barber, S. K. (ORCID:0000000172518755)↗
DOE OSTI2023

Diffusion Probabilistic Modeling for Video Generation

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against six baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality and probabilistic frame forecasting ability for all datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cryogenic aspects of a 20 MW class low-temperature superconducting generator for the renewables industry

In this paper we give a progress update on the cryogenic design for cooling a 20 MW class, partially superconducting generator with stationary field coils for offshore wind renewables industry [1-2]. This is a continuation of an earlier program on a 10 MW system dating back ten years. Whereas this new power rating increase leads to a radial diameter expansion of the superconducting field coils from 4 to > 9.5 m, it maintains the axial length. We show the design based on this scaled up with enlarged diameter. The field coil size increase asks for higher cooling power that leads to a bigger cold box size to accommodate the cryocoolers. In the design, as many as 8 cryocoolers can be accessed and serviced from the nacelle that houses the main cryogenic components. A typical thermal load balance sheet with all components is given and compared against the available cryocooler cooling power at different operating conditions. Due to the cryocoolers’ local point of contact cooling within the nacelle, the temperature gradient of the thermal shield that fully encloses the cold mass with its embedded field coils needs to be balanced out to minimize the heat burden on the field coils. This requires additional analytical efforts and implementation of further design features. The extended nacelle houses the cold box with its cryogenic infrastructure. The interface for the envisaged cryogenic pushbutton closed-loop circulating system remains invisible, requires no handling of cryogenic liquids and is hermetically closed. The field coil diameter increase also leads to a greater initial helium gas storage volume. In addition, it requires higher initial room temperature fill pressure within the toroidal helium vapor storage tanks and in cooling tubes connecting to the cryocoolers. The toroidal helium gas tanks are thermally coupled with the thermal shield so that helium convection inside the storage tank can improve heat transfer and reduce the temperature gradient of the thermal shield. Besides those heat transfer challenges, additional mechanical strain within this large structure is exerted on the torque tubes during initial cooldown and when energizing field coils. Some of those design challenges are quite unexpected, leading to novel workarounds in order to maintain the chosen cooling strategy. Finally, we assess those design limitations in view of further cryogenic scalability with emphasis on manufacturability and assembly.

cryogenics↗