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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impact-Driven Sampling Strategies for Hybrid Attack Graphs

Cyber-Physical Systems (CPSs) have a large input space, with discrete and continuous elements across multiple layers. Hybrid Attack Graph (HAG) provide a flexible and efficient approach to generate attack sequences for a CPS. Analysis and testing of large-scale HAGs are prohibitively costly. We propose a dimension reduction via property-preserving multi-layer graph sampling algorithms. Existing property-preserving graph sampling approaches generate a representative subgraph of an original large-sized graph while preserving the key properties, such as node and edge distribution, clustering coefficients, and betweenness. On the other hand, we propose impact-driven sampling strategies to transform the input data to a lower-dimensional representation while retaining key properties of the data.

Subasi, Omer↗
DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗
DOE OSTI2020

Design and Implementation of Full-Scale Industrial Control System Test Bed for Assessing Cyber-Security Defenses

In response to the increasing awareness of the Ethernet-based threat surface of industrial control systems (ICS), both the research and commercial communities are responding with ICS-specific security solutions. Unfortunately, many of the properties of ICS environments that contribute to the extent of this threat surface (e.g. age of devices, inability or unwillingness to patch, criticality of the system) similarly prevent the proper testing and evaluation of these security solutions. Production environments are often too fragile to introduce unvetted technology and most organizations lack test environments that are sufficiently consistent with production to yield actionable results. Cost and space requirements prevent the creation of mirrored physical environments leading many to look towards simulation or virtualization. Examples in literature provide various approaches to building ICS test beds, though most of these suffer from a lack of realism due to contrived scenarios, synthetic data and other compromises. In this paper, we provide a design methodology for building highly realistic ICS test beds for validating cybersecurity defenses. We then apply that methodology to the design and building of a specific test bed and describe the results and experimental use cases.

Gillen, Rob↗
DOE OSTI2021

Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis

Fusing learning from Earth observations from space and from the ground (e.g., ARM) with newly developed interpretable and generalizable physics-informed model structures to improve the parameterization of clouds and convection and advance the simulation, understanding and analysis of hydrological extreme events. This covers the prescribed foci of (2) predictive modeling using a hierarchy of models and (3) insight gleaned from complex data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Orbital Decay of Double White Dwarfs: Beyond Gravitational-wave Radiation Effects

The traditional description of the orbital evolution of compact-object binaries, like double white dwarfs (DWDs), assumes that the system is driven only by gravitational-wave (GW) radiation. However, the high magnetic fields with intensities of up to gigagausses measured in WDs alert a potential role of the electromagnetic (EM) emission in the evolution of DWDs. We evaluate the orbital dynamics of DWDs under the effects of GW radiation, tidal synchronization, and EM emission by a unipolar inductor generated by the magnetic primary and the relative motion of the nonmagnetic secondary. We show that the EM emission can affect the orbital dynamics for magnetic fields larger than megagausses. We applied the model to two known DWDs, SDSS J0651+2844 and ZTF J1539+5027, for which the GW radiation alone does not fully account for the measured orbital decay rate. We obtain upper limits to the primary's magnetic field strength, over which the EM emission causes an orbital decay faster than observed. The contribution of tidal locking and the EM emission is comparable, and together they can contribute up to 20% to the measured orbital decay rate. We show that the gravitational waveform for a DWD modeled as purely driven by GWs and including tidal interactions and EM emission can have large relative dephasing detectable in the mHz regime of frequencies relevant for space-based detectors like LISA. Therefore, including physics besides GW radiation in the waveform templates is essential to calibrate the GW detectors using known sources, e.g., ZTF J1539+5027, and to infer binary parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The NREL Cyber Range

With the National Renewable Energy Laboratory's (NREL's) cyber range, researchers can replicate cybersecurity scenarios as they would occur on real, complex energy systems. With supercomputing and advanced emulation capabilities, the cyber range allows users to build digital twins of real systems and connect the emulated environment to actual physical devices throughout NREL's laboratories. The space offers unlimited potential to test the frontier of energy systems security.

cyber range↗
DOE OSTI2021

Imaging electric field with electrically neutral particles

It used to think that is impossible to determine/measure electric field inside a physically isolated volume, especially inside an electrically shielded space, because a conventional electric-field sensor can only measure electric field at the location of the sensor, and when an electric-field source is screened by conductive materials, no leakage electric field can be detected. For first time, we experimentally demonstrated that electrically neutral particles, neutrons, can be used to measure/image electric field behind a physical barrier. This work enables a new measurement capability that can visualize electric-relevant properties inside a studied sample or detection target for scientific research and engineering applications.

42 ENGINEERING↗
DOE OSTI2020

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models.

42 ENGINEERING↗
DOE OSTI2026

Comparison of the Effects of Bipolar Membrane Preparation Conditions on the Mechanical Durability and Electrochemical Performance for Electrodialysis Applications

Bipolar membranes (BPMs) are enabling materials for electrochemical conversion technologies such as water electrolysis, fuel cells, CO 2 electrolysis, and electrodialysis (ED) for direct air/ocean capture of CO 2 . However, current BPM durability can suffer from chemical, mechanical, and performance degradation when operated at high current density (ion flux) and physical scale. Therefore, this limits its adoption in a wider applications space. BPMs have several known degradation mechanisms, including chemical breakdown of ion-exchange polymers, loss of junction adhesion, or physical breakdown due to shearing force and pressure swings in an electrodialysis cell. To assess the electrochemical stability and mechanical durability of BPMs under operational conditions, we investigated how fabrication conditions (including preconditioning, hot-pressing temperature and pressure, and catalyst loading) impact the adhesion of custom-made BPMs. T-peel studies were performed ex situ to quantify adhesive forces of BPMs, and bipolar membrane electrodialysis (BPMED) experiments were performed to assess the electrochemical performance of the corresponding BPMs. The results of this systematic comparison indicate that hydration and heated pressing create improved adhesion during the fabrication of BPMs, and BPMED testing shows that these fabrication techniques are not detrimental to the electrochemical performance of the BPMs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Less-Than-Lethal Quick Deploy Inflatable Hall/Door Barrier: VISTA Feasibility Study

Physical protection of public buildings has long been a concern of police and security services where a balance of facility security and personnel safety is vital. Due to the nature of public spaces, the use of permanently installed and deploy-on-demand physical barrier systems must be safe for the legitimate occupants and visitors of that space. Such systems must seek to mitigate the personal and organizational consequences of unintentionally seriously injuring or killing an innocent bystander by slamming a heavy, rigid, and quick-deploying barrier into place. Consideration and implementation of less-than-lethal technologies is necessary to reduce risk to visitors and building personnel. One potential barrier solution is a fast-acting, high-strength, composite airbag barrier system for doorways and hallways to quickly deploy a less-than-lethal barrier at entry points as well as isolate intruders who have already gained access. This system is envisioned to be stored within an architecturally attractive selectively frangible shell that could be permanently installed at a facility or installed in remote or temporary locations as dictated by risk. The system would be designed to be activated remotely (hardwired or wireless) from a Central Alarm Station (CAS) or other secure location.

42 ENGINEERING↗
DOE OSTI2021

Symmetry-protected self-correcting quantum memory in three space dimensions

Whether self-correcting quantum memories can exist at nonzero temperature in a physically reasonable setting remains a great open problem. Furthermore, it has recently been argued that symmetry-protected topological (SPT) systems in three space dimensions subject to a strong constraint—that the quantum dynamics respect a 1-form symmetry—realize such a quantum memory. We illustrate how this works in Walker-Wang codes, which provide a specific realization of these desiderata. In this setting we show that it is sufficient for the 1-form symmetry to be enforced on a subvolume of the system. This strongly suggests that the SPT character of the state is not essential. We confirm this by constructing an explicit example with a trivial (paramagnetic) bulk that realizes a self-correcting quantum memory. We therefore show that the enforcement of a 1-form symmetry on a measure-zero subvolume of a three-dimensional system can be sufficient to stabilize a self-correcting quantum memory at nonzero temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗
DOE OSTI2026

High-precision scale setting with the Ω-baryon mass and gradient flow

The gradient-flow scale 𝑤 0 in lattice QCD is determined using the mass of the Ω − baryon to set the physical scale. Nine ensembles using the highly improved staggered quark (HISQ) action with lattice spacings of 0.15 fm down to 0.04 fm are used, seven of which have nearly physical light-quark masses. Electromagnetic corrections to the Ω − mass are defined in order to compute a pure-QCD Ω mass. The final result is 𝑤 0 = 0.17187⁢(68) fm, corresponding to a relative uncertainty of 0.40% and a central value in good agreement with previous calculations in the literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗