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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 sources NASA NTRS DOE OSTI

DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Causal control design for wave energy converters with finite stroke

Control design is of central importance in the optimization of power generation from ocean wave energy converters (WECs). The problem is made challenging by the fact that the resource is vibratory and stochastic, with a low quality factor. It has recently been shown that when constrained to causality, the optimal feedback controller for a WEC is solvable in closed-form as a sign-indefinite LQG problem, for the case in which the WEC exhibits linear dynamics and quadratic power transmission dissipation. However, most WECs exhibit stroke limits, which must be explicitly accounted for in control design to avoid end-stop collisions. This is important because such collisions lead to damage, and also because the introduction of unmodeled stroke limits can actually destabilize the otherwise-optimal closed loop. We illustrate a control design technique to address this issue, which consists of two design steps. In the first step, LMI techniques are used to optimize the mean power harvested from waves with a known power spectral density, subject to several competing objectives. In the second step, the controller is augmented via a nonlinear passivity-based outer-loop design which guarantees to prevent stroke saturation while also guaranteeing to preserve closed-loop stability. Furthermore, the technique is illustrated using a model of a simple heaving point absorber, and verified in simulation.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Causal horizons, geodesic completeness and stability in slow contraction cosmology

We show that cosmological models with a semi-infinite phase of slow contraction (ekpyrosis) possess a combination of properties that can address several fundamental problems in cosmology, otherwise faced in contracting de Sitter phases or standard big bang expansion. In particular, flat or open slow contraction admits a stable past attractor that asymptotes to Minkowski space and is past geodesically complete, as well as a stable, flat, homogeneous, and isotropic future attractor with negligible Weyl curvature (and, therefore, negligible gravitational entropy). In bouncing cosmologies, this contracting attractor is terminated by a smooth, non-singular bounce that transforms the attractor properties at the end of contraction into the initial conditions for the subsequent expanding phase. Cosmologies incorporating a slow contraction phase have no particle horizon and therefore avoid the causal horizon problem. The past Minkowski attractor also generates an initial spectrum of vacuum-like quantum fluctuations on all wavelengths. Moreover, because the averaged expansion rate along past-directed geodesics is non-positive, models incorporating a semi-infinite phase of slow contraction also evade the Borde–Guth–Vilenkin theorem. By contrast, contracting de Sitter space possesses a finite particle horizon and becomes unstable in the presence of scalar fields, matter, or radiation.

Cosmology and Nongalactic Astrophysics (astro-ph.C↗

DOE OSTI2025

Exploring Causal Relationships and Adjustment Timescales of Aerosol-Cloud Interactions in Geostationary Satellite Observations and CAM6 Using Wavelet Phase Coherence Analysis

We present for the first time within the cloud physics context, the application of wavelet phase coherence analysis to disentangle counteracting physical processes associated with the lead-lag phase difference between cloud-proxy liquid water path (LWP) and aerosol-proxy cloud droplet number concentration ( N d ) in an Eulerian framework using satellite-based observations and climate model outputs. This approach allows us to identify the causality and dominant adjustment timescales governing the correlation between LWP and N d . Satellite observations indicate a more prevalent positive correlation between daytime LWP and N d regardless of whether LWP leads or lags N d . The positive cloud water response, associated with precipitation processes, typically occurs within 1 hr, while the negative response resulting from entrainment drying, usually takes 2–4 hr. CAM6 displays excessively rapid negative responses along with overly strong negative cloud water response and insufficient positive response, leading to a more negative correlation between LWP and N d compared to observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

The causal relation between turbulent particle flux and density gradient

A technique for detecting the causal relationship between fluctuating signals is used to investigate the relation between flux and gradient in fusion plasmas. Both a resistive pressure gradient driven turbulence model and experimental Langmuir probe data from the TJ-II stellarator are studied. It is found that the maximum influence occurs at a finite time lag (non-instantaneous response) and that quasi-periodicities exist. Furthermore, the model results show very long range radial influences, extending over most of the investigated regions, possibly related to coupling effects associated with plasma self-organization. These results clearly show that transport in fusion plasmas is not local and instantaneous, as is sometimes assumed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Causality, intermittence, and crossphase evolution during confinement transitions in the TJ-II stellarator

In this work, we study spontaneous electron to ion root transitions in TJ-II using Langmuir probes. By scanning the probe position on a shot to shot basis, we reconstruct a spatiotemporal map of the evolution of important turbulent quantities in the plasma edge region. We pay particular attention to the evolution of the cross phase between transport-relevant variables, showing the spatiotemporal evolution of this quantity for the first time, revealing the outward propagation of the changes associated with the transition. Additionally, we also compute the intermittence parameter, which allows us to conclude that the turbulence, although its amplitude increases, condenses in a reduced number of dominant modes and becomes less bursty. The causal relationship between variables is studied using the transfer entropy, clarifying the interactions between the main variables and offering a rather complete picture of the complex evolution of the plasma across the confinement transition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Initial data for first-order causal viscous conformal fluids in general relativity

We solve the Einstein constraint equations for a first-order causal viscous relativistic hydrodynamic theory in the case of a conformal fluid. For such a theory, a direct application of the conformal method does not lead to a decoupling of the equations, even for constant-mean curvature initial data. We combine the conformal method applied to a background perfect fluid theory with a perturbative argument in order to obtain the result.

Disconzi, Marcelo (ORCID:0000000234497778)↗

DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them

Abstract Before we attempt to (approximately) learn a function between two sets of observables of a physical process, we must first decide what the inputs and outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding “the right quantities” to relate through such a function, and then proceeding to learn it. This is accomplished by first processing simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple observation processes of the system. We determine (i) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function); and (ii) what information is unrelated to these common observables, therefore particular to each observation process, and not contributing to the desired function. Any data-driven technique can subsequently be used to learn the input–output relation—from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular “twists” of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations from one process to entire level sets of measurements of the second process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures “now,” while the second observation process measures “in the future,” the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution.

Sroczynski, David W.↗

DOE OSTI2020

Casimir force, causality, and the Gurzhi model

An extended Drude model, termed the Gurzhi model, which takes into account the electron-phonon and electron-electron interactions, is applied to calculate the Casimir force between two metallic plates. It is shown that although the dielectric permittivity of the Gurzhi model has a first-order pole in the upper half-plane of complex frequencies and, thus, violates the causality principle, it can be used in a restricted frequency interval in combination with the experimental permittivity determined by the optical data for the complex index of refraction. The imaginary part of the Gurzhi dielectric permittivity of Au at low frequencies demonstrates better agreement with the permittivity given by the optical data than the simple Drude model. The Casimir pressure between two Au plates is computed using the Gurzhi, Drude, and plasma model approaches, taking into account the optical data, as well as with the simple Drude and plasma models. Finally, the contribution of the electron-electron scattering to the Casimir pressure is estimated. Although a comparison with the measurement data of two precise experiments shows that the Gurzhi model does not resolve the Casimir puzzle, the obtained results suggest further clarification of this fundamental problem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Sonic horizons and causality in phase transition dynamics

A system gradually driven through a symmetry-breaking phase transition is subject to the Kibble-Zurek mechanism (KZM). As a consequence of the critical slowing down, its state cannot follow local equilibrium, and its evolution becomes nonadiabatic near the critical point. In the simplest approximation, that stage can be regarded as an “impulse” where the state of the system remains unchanged. It leads to the correct KZM scaling laws. However, such a “freeze-out” might suggest that the coherence length of the nascent order parameter remains unchanged as the critical region is traversed. By contrast, the original causality-based discussion emphasized the role of the sonic horizon: Domains of the broken symmetry phase can expand with a velocity limited by the speed of the relevant sound. This effect was demonstrated in the quantum Ising chain where the dynamical exponent z = 1 and quasiparticles excited by the transition have a fixed speed of sound. To elucidate the role of the sonic horizon, in this paper we study two systems with z > 1 where the speed of sound is no longer fixed, and the fastest excited quasiparticles set the size of the sonic horizon. Their effective speed decays with increasing transition time. In the extreme case, the dynamical exponent z can diverge such as in the Griffiths region of the random Ising chain where the localization of excited quasiparticles freezes the growth of the correlation range when the critical region is traversed. Of particular interest is an example with z < 1 , the long-range extended Ising chain, where there is no upper limit to the velocity of excited quasiparticles with small momenta. Initially, the power-law tail of the correlation function grows adiabatically, but in the nonadiabatic stage it lags behind the adiabatic evolution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Stochastic fluctuations in relativistic fluids: Causality, stability, and the information current

We develop a general formalism for introducing stochastic fluctuations around thermodynamic equilibrium which takes into account, for the first time, recent developments in the causality and stability properties of relativistic hydrodynamic theories. The method is valid for any covariantly stable theory of relativistic viscous fluid dynamics derived from a covariant maximum entropy principle. We illustrate the formalism with some applications, showing how it could be used to consistently introduce fluctuations in a model of relativistic heat diffusion and in conformally invariant Israel-Stewart theory in a general hydrodynamic frame. Furthermore, the latter example is used to study the hydrodynamic frame dependence of the symmetric two-point function of fluctuations of the energy-momentum tensor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Causal fermion states in magnetic field in a relativistic rotating frame and electromagnetic radiation by a rapidly rotating charge

We consider the Dirac field uniformly rotating with angular velocity Ω and also subject to the constant magnetic field B directed along the rotation axis. The causal states are constrained to the interior of the light cylinder of radius c / Ω . When this radius is smaller than the system size, as in the quark-gluon plasma, the effect of the boundary on the fermion spectrum is critical. We derive the fermion spectrum and study its properties. We compute the intensity of the electromagnetic radiation emitted due to transitions between the fermion states. We study its dependence on energy and angular momentum for different values of the angular velocity and the magnetic field. Rotation has enormous impact on the electromagnetic radiation by the quark-gluon plasma with or without the magnetic field. Published by the American Physical Society 2024

Buzzegoli, Matteo (ORCID:0000000221145431)↗

DOE OSTI2024

Enhancing Power Grid Resilience with Causal Loops Diagram and Bayesian Networks

Enhancing power grid resilience through improved analysis and planning of Distributed Energy Resources is a key for power system planner. This paper explores the integration of Causal Loop Diagrams (CLDs) and Bayesian Networks (BNs) for enhancing resilience in power systems, focusing on Distributed Energy Resources (DER) planning. By automating CLD analysis in Python's matplotlib, we present a tool for rapid model validation and structural accuracy, crucial for power system planners. This hybrid approach utilizes BNs for inferential depth and CLDs for dynamic system modeling, offering a comprehensive framework for policy formulation and collaborative strategy development against disruptions. Here, we highlight the tool's capability to identify and analyze interconnected feedback loops, facilitating a deeper understanding of DER integration's impact on network resilience. This work aims to bridge quantitative analysis and qualitative insights, addressing the limitations of each method while providing a robust model for power system resilience assessment.

14 SOLAR ENERGY↗

DOE OSTI2018

Evidence for lack of direct causality between pain and affective disturbances in a rat peripheral neuropathy model

Chronic pain is frequently accompanied by the manifestation of emotional disturbances and cognitive deficits. While a causality relation between pain and emotional/cognitive disturbances is generally assumed, several observations suggest a temporal dissociation and independent mechanisms. We therefore studied Sprague‐Dawley rats that presented a natural resistance to pain manifestation in a neuropathy model (spared nerve injury [SNI]) and compared their performance in a battery of behavioral paradigms—anxiety, depression and fear memory—with animals that presented a pain phenotype. Afterward, we performed an extensive volumetric analysis across prefrontal, orbitofrontal and insular cortical areas. The majority of SNI animals manifested mechanical allodynia (low threshold [LT]), but 13% were similar to Sham controls (high threshold [HT]). Readouts of spontaneous hypersensivity (paw flinches) were also significantly reduced in HT and correlated with allodynia. To increase the specificity of our findings, we segregated the SNI animals in those with left (SNI‐L) and right (SNI‐R) lesions and the lack of association between pain and behavior still remains. Left‐lesioned animals, independent of the LT or HT phenotype, presented increased anxiety‐like behaviors and decreased well‐being. In contrast, we found that the insular cortex (agranular division) was significantly smaller in HT than in LT. To conclude, pain and emotional disturbances observed following nerve injury are to some extent segregated phenomena. Also, HT and LT SNI presented differences in insular volumes, an area vastly implicated in pain perception, suggesting a supraspinal involvement in the manifestation of these phenotypes.

Guimarães, Marco R.↗

DOE OSTI2015

Isolating causal pathways between flow and fish in the regulated river hierarchy

Unregulated river systems are organized in a hierarchy in which large-scale factors (i.e., landscape and segment scales) influence local habitats (i.e., reach, meso-, and microhabitat scales), and both differentially exert selective pressures on biota. Dams, however, create discontinua in these processes and change the hierarchical structure. We examined the relative roles of hydrology and other instream factors, within a hierarchical landscape context, in organizing fish communities in regulated and unregulated tributaries to the Upper Tennessee River, USA. We also used multivariate regression trees to identify factors that partition fish assemblages based on trait similarities, irrespective of spatial scale. Then, we used classical path analysis and structural equation modeling to evaluate the most plausible hierarchical causal structure of specific trait-based community components, given the data. Both statistical approaches suggested that river regulation affects stream fishes through a variety of reach-scale variables, not always through hydrology itself. Though we observed different changes in flow, temperature, and biotic responses according to regulation types, the most predominant path in which dam regulation affected biota was via temperature alterations. Diversion dams had the strongest effects on fish assemblages. Diversion dams reduced flow magnitudes, leading to declines in fish richness but increased temperatures, leading to lower abundances in equilibrium species and nest guarders. Peaking and run-of-river dams increased flow variability, leading to lower abundances in nest-guarding fishes. Flow displayed direct relationships with biotic responses; however, results indicated that changes in temperature and substrate had equal, if not stronger, effects on fish assemblage composition. The strength and nature of relationships depended on whether flow metrics were standardized for river size. Here, we suggest that restoration efforts in regulated rivers focus on improving flow conditions in conjunction with temperature and substrate restoration.

58 GEOSCIENCES↗

DOE OSTI2023

A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.

Cho, Wendy K. Tam↗

DOE OSTI2021

y0-causal-inference/y0

❓y0 (pronounced "why not?") is for causal inference in Python: a software library intended to support the scientific discovery process.

Hoyt, Charles Tapley↗