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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Adversarial uncertainty quantification in physics-informed neural networks

Here, we introduce a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as surrogates of physical systems in which the cost of data acquisition is high, and training data-sets are typically small. This provides a flexible framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations that entirely bypasses the need for repeatedly sampling expensive experiments or numerical simulators. Moreover, we demonstrate the effectiveness of our approach through a series of examples involving uncertainty propagation in non-linear conservation laws, and the discovery of constitutive laws for flow through porous media directly from noisy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Gravitational probes of dark matter physics

The nature of dark matter is one of the most pressing questions in physics. Yet all our present knowledge of the dark sector to date comes from its gravitational interactions with astrophysical systems. Moreover, astronomical results still have immense potential to constrain the particle properties of dark matter in the near future. Here, we introduce a simple 2D parameter space which classifies models in terms of a particle physics interaction strength and a characteristic astrophysical scale on which new physics appears, in order to facilitate communication between the fields of particle physics and astronomy. We survey the known astrophysical anomalies that are suggestive of non-trivial dark matter particle physics, and present a theoretical and observational program for future astrophysical measurements that will shed light on the nature of dark matter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis

Machine learning (ML) models are gaining popularity in structural health monitoring (SHM) because of their ability to learn the complex relationship between damage and sensor data. However, the lack of sufficient experimental data for structures with different degrees of damage is a key problem in training ML models for SHM. This problem can be alleviated by using physics-based models to generate the required training data to build physics-informed ML (PIML) models for SHM. However, it takes significant computational effort to perform enough high-fidelity simulations of the diagnostic test. It is thus desirable to know whether the available computational resource budget should be expended on numerous low-fidelity physics simulations, or a small number of high-fidelity simulations, or their combination. In this paper, we investigate this aspect of generating adequate training data for PIML, by constructing multi-fidelity PIML models. We evaluate the performance of several PIML models, trained with different amounts of low-fidelity and high-fidelity data, in locating hidden cracks in concrete structures using a nonlinear dynamics-based diagnosis technique. Here, we find that high-fidelity physics simulations that do not cover the (test and damage) parameter space do not improve the performance of diagnostic PIML models built using data from many low-fidelity physics simulations.

42 ENGINEERING↗
DOE OSTI2019

Effects of Model Resolution, Physics, and Coupling on Southern Hemisphere Storm Tracks in CESM1.3

Two high–resolution versions of a Coupled Earth System Model (CESM1.3: 0.25° atmosphere, 1° ocean; CESM1.1: 0.25° atmosphere, 0.1° ocean) are compared to the standard resolution CESM1.1 and CESM1.3 (1° atmosphere, 1° ocean). The CESM1.3 versions are documented, and the consequences of model resolution, air–sea coupling, and physics in the atmospheric models are studied with regard to storm tracks in the Southern Hemisphere as represented by 850–hPa eddy kinetic energy. Increasing the resolution from 1° to 0.25° in the atmosphere (same physics) coupled to the 1° ocean intensifies the strength of the storm tracks closer to observations. The 0.25° atmosphere with the older CESM1.1 physics coupled to the 0.1° ocean has fewer low clouds, warmer Southern Ocean sea surface temperatures, a weaker meridional temperature gradient, and a degraded storm track simulation compared to the 0.25° atmosphere with CESM1.3 physics coupled to the 1° ocean. Furthermore, deficient physics in the atmospheric model can negate the gains attained by higher resolution in atmosphere and ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks

Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

Science & Technology - Other Topics↗
DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗
DOE OSTI2026

Radioactive molecules as laboratories of fundamental physics

Radioactive molecules provide a new platform in the search for new physics, at energy scales complementary to those probed by high-energy particle colliders. Here, by combining enhancements from nuclear properties with the sensitivity and control offered by molecular structure, experiments with radioactive molecules offer great reach in the search for physics beyond the standard model. Progress in this field is being driven by advances in the production and control of radioactive molecules, alongside the development of new experimental tools and theoretical techniques. In this Perspective, we discuss the current status and future prospects of this rapidly developing, interdisciplinary field at the intersection of nuclear physics, atomic and molecular physics and particle physics.

Jadbabaie, A. [Massachusetts Institute of Technolo↗
DOE OSTI2020

Evaluating Resilience of Water Distribution Networks to Operational Failures from Cyber-Physical Attacks

Water cyber-physical systems (CPSs) have gained increasing interest to improve operational efficiency and reliability. However, due to growing exposure to cyber-physical attacks, cybersecurity and resilience against the attacks have become significant concerns. There have been efforts to improve cybersecurity in water CPSs, yet few attempts to investigate resilience against cyber-physical attacks. This study contributes to characterizing resilience of a water CPS and investigating potential resilience strategies. An advanced resilience measure integrating withstanding, absorptive, adaptive, and restorative capability of a system is proposed and applied to the C-town water distribution network (WDN) for 15 failure set scenarios with a pressure-driven hydraulic simulation. The results provide identification of failure sets and unfavorable operational conditions that make the system more vulnerable to the cyber-physical attacks and in turn produce low resilience and capabilities. It is also found that, after recovery of a disrupted component, adjustment of overall operational interactions across system components is needed for complete restoration of disrupted functionality. The findings provide insights on infrastructure investment with resilience strategies in cyber and physical water system domains.

Shin, Sangmin↗
DOE OSTI2024

PhysBERT: A text embedding model for physics scientific literature

The specialized language and complex concepts in physics pose significant challenges for information extraction through Natural Language Processing (NLP). Central to effective NLP applications is the text embedding model, which converts text into dense vector representations for efficient information retrieval and semantic analysis. In this work, we introduce PhysBERT, the first physics-specific text embedding model. Pre-trained on a curated corpus of 1.2 × 106 arXiv physics papers and fine-tuned with supervised data, PhysBERT outperforms leading general-purpose models on physics-specific tasks, including the effectiveness in fine-tuning for specific physics subdomains.

Hellert, Thorsten (ORCID:0000000227970926)↗
DOE OSTI2024

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

New physics search at the CEPC: a general perspective

A next generation, high-intensity electron-positron collider “Higgs factory”, such as the Circular Electron-Positron Collider (CEPC), is among the highest priority for the global high energy collider physics community. The CEPC can provide unprecedented opportunities for making fundamental discoveries and providing decisive insights in the quest for a “New Standard Model (SM)” of nature’s fundamental interactions. The CEPC could: 1) Identify the origin of matter, especially the mechanism related to the first-order phase transition in the early Universe, which could produce a detectable gravitational wave signal. 2) Discover dark matter, particularly dark matter particles with a mass between one tenth and 100 times the proton mass. 3) Observe an array of new physics smoking guns, with sensitivities orders of magnitude better than those of existing facilities. The SM of Particle Physics is a triumph of the past half a century, as it predicts and interprets almost all the phenomena observed in experiments from the highest energies with colliders to low energy “tabletop” studies. On the other hand, deep mysteries exist concerning the most fundamental interactions of matter and the space-time fabric of the Universe, including the nature of dark matter, the origin of “visible” matter, the vast hierarchy of elementary particle masses, the quantum nature of gravity, and the mechanism of inflation. These mysteries challenge us to look for “new physics” beyond the SM and General Relativity. Indeed, physicists believe that the SM is simply a low-energy effective theory that reflects aspects of the more profound theory that answers the aforementioned mysteries. Uncovering this “New SM”, the profound theory who supports the SM is the primary mission for particle physics in the post-Higgs boson era.

Ai 艾, Xiaocong 小聪 [Zhengzhou University (China); e↗
DOE OSTI2026

Explainable physics-based constraints on reinforcement learning for accelerator optimization

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments.

explainability↗
DOE OSTI2019

Modelling baryonic physics in future weak lensing surveys

Modifications of the matter power spectrum due to baryonic physics are one of the major theoretical uncertainties in cosmological weak lensing measurements. Developing robust mitigation schemes for this source of systematic uncertainty increases the robustness of cosmological constraints, and may increase their precision if they enable the use of information from smaller scales. Here we explore the performance of two mitigation schemes for baryonic effects in weak lensing cosmic shear: the principal component analysis (PCA) method and the halo-model approach in hmcode. We construct mock tomographic shear power spectra from four hydrodynamical simulations, and run simulated likelihood analyses with cosmolike assuming LSST-like survey statistics. With an angular scale cut of ℓmax < 2000, both methods successfully remove the biases in cosmological parameters due to the various baryonic physics scenarios, with the PCA method causing less degradation in the parameter constraints than hmcode. For a more aggressive ℓmax = 5000, the PCA method performs well for all but one baryonic physics scenario, requiring additional training simulations to account for the extreme baryonic physics scenario of Illustris; hmcode exhibits tensions in the 2D posterior distributions of cosmological parameters due to lack of freedom in describing the power spectrum for $k \gt 10\ h^{-1}\, \mathrm{Mpc}$. We investigate variants of the PCA method and improve the bias mitigation through PCA by accounting for the noise properties in the data via Cholesky decomposition of the covariance matrix. Our improved PCA method allows us to retain more statistical constraining power while effectively mitigating baryonic uncertainties even for a broad range of baryonic physics scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Learning from metastable symmetric-tilt grain boundaries using physics-based descriptors

Grain boundaries (GBs) govern critical properties of polycrystalline materials. Although significant advancements have been made in characterizing minimum energy and ordered GBs, real GBs are seldom found in such well-defined states. This diversity of atomic arrangements in metastable states makes it challenging to establish structure-property relationships with physical insights. Here, to address this challenge, we use data-driven methods to explore these relationships and examine the underlying physics. In this study, we utilize a large atomistic database (~5000) of minimum energy and metastable states of symmetric-tilt copper GBs, combined with physically motivated local atomic environment (LAE) descriptors [strain functional descriptors (SFDs)], to predict GB properties and gain physical insights. Our regression models exhibit robust predictive capabilities using only 19 descriptors, generalizing to atomic environments in nanocrystals. A significant highlight of our work is the integration of an unsupervised method with SFDs to elucidate LAEs at GBs and their role in determining properties. The model, trained on these minimum energy and metastable GBs using SFDs, predicts the properties of unseen nanocrystals with good accuracy. Our research underscores the role of a physics-based representation of LAEs and the efficacy of data-driven methods in establishing GB structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Real-Time Cyber-Physical False Data Attack Detection in Smart Grids Using Neural Networks

Attacks on cyber-physical systems have recently increased in frequency, impact, and publicity. In this paper, a cyber-physical false data attack detection mechanism is proposed to protect the operation of power transmission and distribution systems by automatically inferring underlying physical relationships using cross-sensor analytics in order to detect sensor failures, replay attacks, and other data integrity issues in real-time. We investigate a neural network based mechanism acting on voltage and current readings resulting from a wide variety of load conditions on the IEEE 30-bus power system standard and compare its performance with a support vector machine based mechanism. Experiments showed that 99% detection accuracy of replay attacks was achieved using the proposed neural network mechanism. More importantly, we showed that the best approach was to not create physics-based features using what we knew about the system, but rather to use a neural network to automatically learn the laws, and then use the outputs of that to build a classifier to identify whether and where data spoofing occurs. Thus, it is preferable to infer and exploit the physics using a single machine learning solution

Ferragut, Erik M.↗
DOE OSTI2014

Electromagnetic design, engineering development and magnetic qualification of a horizontal layered scaled magnet for physics experiments

Neutrino detectors around the world have shown evidence that these weakly interacting, little-understood particles are not really mass less, as was thought so far. Not only do they have non-zero masses, different species (or flavors) of neutrinos seem to mix and oscillate into one another as they traverse through the cosmos. If this is true, this is not only one of the first pieces of evidence for physics beyond the so-called Standard Model of Particle Physics, but would also have great impact on diverse fields such as nuclear and particle physics, astrophysics and cosmology. It is thus imperative to study the details of the interactions of these particles. These will be detected by means of an iron calorimeter (ICAL), comprising detectors sandwiched in alternate layers of soft magnetic iron. The iron core is magnetized to allow bending of the charged particles. The direction and the energy of the original incoming neutrino, that caused the interaction, can then be accurately determined. Racetrack coils through slots cut in the iron core create a uniform magnetic field in the iron core. A highly uniform magnetic field in the soft iron core is required to accurately determine the mass and energy of incident particle. The charged particles bend in this magnetic field; oppositely charged particles bending in opposite directions. The charge, energy and momentum of the emitted particle is thus determined. An electromagnet was designed as a laboratory facility for conducting experimental studies in particle physics. This paper discusses the electromagnetic design, development, fabrication, magnetic qualification and magnetic measurement issues of an electromagnet developed for physics studies. (author)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Detecting Sensors and Inferring their Relations at Level-0 in Industrial Cyber-Physical Systems

While there exist tools and techniques to discover, identify, map, and analyze cyber physical components at higher levels of the cyber space, there is a lack of capabilities to reach down to the sensors at the bottom-most levels, such as levels 0 and 1 of the Purdue Enterprise Reference Model for cyber-physical systems (CPS). Conventional information technology (IT)-based tools reach as far as the network-side of programmable logic controllers, but are inadequate to access and analyze the physical side of the CPS infrastructure that directly interfaces with the actual physical processes and systems. In this paper, we present our research and development efforts aimed at addressing this gap, by building a system called Deep-cyberia (Deep Cyber-Physical System Interrogation and Analysis) that incorporates algorithms and interfaces aimed at uncovering sensors and computing estimates of correlations among them.

Perumalla, Kalyan↗