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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct observation of two-dimensional magnons in atomically thin CrI 3

Magnons are collective spin excitations in crystals with long-range magnetic order. The emergent van der Waals magnets provide a highly tunable platform to explore magnetic excitations in the two-dimensional limit with intriguing properties, manifesting from their honeycomb lattice structure and switchable magnetic configurations. Here, we report the direct observation of two-dimensional magnons through magneto-Raman spectroscopy with optical selection rules determined by the interplay between crystal symmetry, layer number and magnetic states in atomically thin CrI 3 . In monolayers, we observe an acoustic magnon mode at ~0.3 meV. It has strict cross-circularly polarized selection rules locked to the magnetization direction that originates from the conservation of angular momentum of photons and magnons dictated by three-fold rotational symmetry. Additionally, we reveal optical magnon modes at ~17 meV. This mode is Raman silent in monolayers, but optically active in bilayers and bulk due to a relaxation of the parity criterion resulting from the layer index. In the layered antiferromagnetic states, we directly resolve two degenerate optical magnon modes with opposite angular momentum and conjugate optical selection rules. From these measurements, we quantitatively extract the spin-wave gap, magnetic anisotropy and intralayer and interlayer exchange constants, and establish two-dimensional magnets as a new platform for exploring magnon physics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Diagnosis of Alzheimer’s disease using plasma biomarkers adjusted to clinical probability

Abstract Recently approved anti-amyloid immunotherapies for Alzheimer’s disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing.

Cell Biology↗
DOE OSTI2024

Upper and lower bounds on phase-space rearrangements

Broad classes of plasma phenomena can be understood in terms of phase-space rearrangements. For example, the net effect of a wave–particle interaction may consist of moving populations of particles from one region of phase space to another. Different phenomena drive rearrangements that obey different rules. When those rules can be specified, it is possible to calculate bounds that limit the possible effects the rearrangement could have (such as limits on how much energy can be extracted from the particles). This leads to two problems. The first is to understand the mapping between the allowed class of rearrangements and the possible outcomes that these rearrangements can have on the overall distribution. The second is to understand which rules are appropriate for which physical systems. There has been recent progress on both fronts, but a variety of interesting questions remain unanswered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A Structural Design Approach Tailored for the Rapid Preliminary Design of Microreactor Components

High-temperature microreactors can play a role in developing reliable, portable energy sources for off-grid remote locations, microgrid concepts, and industrial process heat. Portability and passive safety criteria tend to skew microreactor structural component designs toward complex geometries, high thermal stresses, and design bases with large numbers of startup/shutdown cycles. Current design rules, as typified by Section III of the American Society of Mechanical Engineers (ASME) Boiler & Pressure Vessel Code, are less than optimal for these conditions, particularly for preliminary component designs where developers need to rapidly consider a large number of potential component configurations. This paper presents a design method targeted toward rapid, efficient evaluation of preliminary component designs using modern finite element analysis. The new method retains key connections with the ASME Code rules and design data while streamlining the design approach. This paper presents the design method, several verification examples illustrating the similarities and differences between the new method and the current ASME rules, and the application of the new approach to the evaluation of a test article mimicking key features of a heat pipe–cooled microreactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Simulation-based analysis of different curb space type allocations on curb performance

Curbspace is a limited resource in urban areas. Delivery, ridehailing and passenger vehicles must compete for spaces at the curb. Cities are increasingly adjusting curb rules and allocating curb spaces for uses other than short-term paid parking, yet they lack the tools or data needed to make informed decisions. In this research, we analyze and quantify the impacts of different curb use allocations on curb performance through simulation, covering various mixes of curbspace uses (bus stops, paid parking, passenger pick-up/drop-off zones, and commercial vehicle loading zones), parking rules, and driver rule compliance. Three metrics (including two new ones) are developed to evaluate the performance of the curb, covering productivity and accessibility of passengers and goods, and CO 2 emissions. The metrics are calculated for each scenario across a wide range of input parameters (traffic volume, parking demand rate, vehicle dwell time, and street design speed) and compared to each other and to a baseline scenario. This work can inform policy decisions by providing municipalities a tool to analyze various curb management strategies and choose the ones that produce results more in line with their policy goals.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Multiplicative Shot-Noise: A New Route to Stability of Plastic Networks

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events of two “parent” processes. Here we propose a multiplicative shot-noise model that can capture the behaviors of a broad range of such natural phenomena, and analytically derive an approximation that accurately predicts its statistics. We apply our results to study the effects of a multiplicative synaptic plasticity rule that was recently extracted from measurements in physiological conditions. Using mean-field theory analysis and network simulations, we investigate how this rule shapes the connectivity and dynamics of recurrent spiking neural networks. The multiplicative plasticity rule is shown to support efficient learning of input stimuli, and it gives a stable, unimodal synaptic-weight distribution with a large fraction of strong synapses. The strong synapses remain stable over long times but do not “run away.” Our results suggest that the multiplicative shot-noise offers a new route to understand the tradeoff between flexibility and stability in neural circuits and other dynamic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distributed Inference with Sparse and Quantized Communication

Here, we consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just 1 bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.

42 ENGINEERING↗
DOE OSTI2025

Secure biosystems design in Saccharomyces cerevisiae establishes effective biocontainment strategies and mechanisms of escape

The widespread application of recombinant DNA and synthetic biology approaches for microbial metabolic engineering pursuits has motivated the development of biocontainment strategies, targeting safe and secure deployment of genetically modified microorganisms (GMMs). However, the design rules and mechanistic drivers governing biocontainment efficacy, as well as impacts of biocontainment upon microbial fitness, remain to be comprehensively evaluated, hindering predictive design and application of these strategies. We have developed a platform for high-resolution analysis of a transactivated kill switch in laboratory and industrial strains of Saccharomyces cerevisiae to assess modes of biocontainment escape and establish design rules for development of kill switch systems in diverse microbes. A camphor-regulated, RelE toxin system was systematically deployed to assess the impacts of differential kill switch copy number and ploidy in laboratory vs industrial strains. CRISPR-mediated integration of the biocontainment system at various loci revealed rapid escape events driven, in part, by mutations to both the Cam-transactivator (cam-TA) and RelE toxin. Genetic engineering enabled recapitulation of escape phenotypes, confirming mechanisms of escape and establishing structure-function relationships in the cam-TA system. Interestingly, genomic resequencing of escape mutants also revealed a series of off-target mutations, implicating additional modes of kill switch escape. Multi-copy integration of the kill switch system mitigated these effects by orders of magnitude, without compromising the biosynthetic capacity of the microbes, but proved insufficient to establish sustained biocontainment. The resultant data define a series of key design rules for next-generation biocontainment strategies and add to a growing foundational knowledge base targeting establishment of secure biosystems designs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Fracture length data for geothermal applications

Fracture lengths govern permeability and are unknowns in geothermal assessment. Along their lengths, fracture widths vary due to growth by linkage. Under the influence of diagenesis, narrow widths seal, breaking porosity continuity and reducing open length. The largest range of widths and thus susceptibility to fill occurs where fractures are linked by narrow segments. Outcrops of a geothermal target, Cambrian Potsdam quartz arenite, contain opening-mode fractures having lengths spanning five orders of magnitude from 0.082 mm to 17.9 m. Combined lengths measured at a range of scales can be described by power laws, but at a given image resolution, lengths are best fit by exponential functions. Owing to preferential sealing of small fractures, open fractures follow exponential functions, but values depend on rules for designating fractures as continuous. En échelon segments, offset 10 mm, are connected by narrow fractures or microfractures (hard linked) not evident on outcrop 1 m-elevation LiDAR or 30 m-height drone images. A rule that identifies where narrow and likely connected segments are located can yield lengths meaningful for flow simulation. Depending on diagenesis, continuity rules can halve or double average and maximum length values. Length values from outcrop for geothermal applications should be adjusted based on wellsite-specific diagenesis information.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation

Neuromorphic computing is emerging as a promising Beyond Moore computing paradigm that employs event-triggered computation and non-von Neumann hardware. Spike Timing Dependent Plasticity (STDP) is a well-known bio-inspired learning rule that relies on activities of locally connected neurons to adjust the weights of their respective synapses. In this work, we analyze a basic STDP rule and its sensitivity on the different hyperparameters for training spiking neural networks (SNNs) with supervision, customized for a neuromorphic hardware implementation with integer weights. We compare the classification performance on four UCI datasets (iris, wine, breast cancer and digits) that depict varying levels of complexity. We perform a search for optimal set of hyperparameters using both grid search and Bayesian optimization. Through the use of Bayesian optimization, we show the general trends in hyperparameter sensitivity in SNN classification problem. With the best sets of hyperparameters, we achieve accuracies comparable to some of the best performing SNNs on these four datasets. With a highly optimized supervised STDP rule we show that these accuracies can be achieved with just 20 epochs of training.

Kulkarni, Shruti↗
DOE OSTI2022

Genetic programming for understanding cognitive biases that generate polarization in social networks

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.

Gunaratne, Chathika↗
DOE OSTI2023

Design tradeoffs between traditional hexagonal and emerging cubic In X Ga (1–X) N/GaN-based green light-emitting diodes

Here, we report on the design tradeoffs between traditional hexagonal and emerging cubic In X Ga (1–X) N/GaN -based green (520 nm≤ λ ≤550 nm) light-emitting diodes with special emphasis on the electron blocking layer, number of quantum wells, and thicknesses of quantum wells and barriers. We identified three crucial design rules for cubic green light-emitting diodes: (1) no need for an electron blocking layer; (2) use of a wide quantum well; and (3) choice of thin quantum barriers in multi-quantum well light-emitting diode designs. These design rules increase the internal quantum efficiency of cubic green light-emitting diodes by ~30.5% under 100A/cm 2 injection with respect to traditional designs. Overall, the design rules of cubic light-emitting diodes and their differentiating nature from the traditional, hexagonal ones are crucial for the advent of next-generation cubic light-emitting diodes.

42 ENGINEERING↗
DOE OSTI2020

UTrain Manual for Training Administrators

This Institutional Training Administrative Procedure provides the business rules that must be followed when working in UTrain. As commercial off-the-shelf software with customizations, UTrain cannot address all the implementation business requirements, and thus, administrative business rules and processes were defined and documented. Many business rules were incorporated into the UTrain training materials. UTrain is the Laboratory’s official learning management system. UTrain is the system of record for all Laboratory training, qualification, certification, and worker authorization. All training at LANL must be documented in UTrain. The detailed steps of how to operate UTrain are contained in the UTrain courses and training manuals and in the UTrain job aids, not in this document.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Preliminary design analysis workflow for Division 5 HHA-3200 requirements for graphite core components

This report presents a design analysis workflow for graphite core components and assemblies, based on the design rules of ASME Boiler Pressure and Vessel Code, Section III, Division 5, Article HHA-3000. The workflow contains three stages: developing the design of the graphite core component, modeling the component with the finite element software MOOSE, and assessing if the component passes/fails the criteria of the HHA-3000 design rules. Since the design rules use probabilistic metrics specifically established to evaluate brittle materials, we developed a python library that performs all the statistical calculations necessary for the evaluations of the HHA-3000 criteria.

97 MATHEMATICS AND COMPUTING↗