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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

EdgeAI: Machine learning via direct attached accelerator for streaming data processing at high shot rate x-ray free-electron lasers

We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware

The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Processing Units and Reconfigurable Dataflow Units. We show the landscape of training times with a goal of full model retraining in under 15 min. Although a full from scratch retraining of a model may not be required in all cases, we nevertheless present an example of the application of emerging computational hardware for adapting machine learning models to changing environments in real-time, during streaming data acquisition, at the rates expected for the data fire hoses of accelerator-based user facilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

A comparison of transport algorithms for premixed, laminar steady state flames

The effects of different methods of approximating multispecies transport phenomena in models of premixed, laminar, steady state flames were studied. Five approximation methods that span a wide range of computational complexity were developed. Identical data for individual species properties were used for each method. Each approximation method is employed in the numerical solution of a set of five H2-02-N2 flames. For each flame the computed species and temperature profiles, as well as the computed flame speeds, are found to be very nearly independent of the approximation method used. This does not indicate that transport phenomena are unimportant, but rather that the selection of the input values for the individual species transport properties is more important than the selection of the method used to approximate the multispecies transport. Based on these results, a sixth approximation method was developed that is computationally efficient and provides results extremely close to the most sophisticated and precise method used.

Coffee, T. P.↗
NASA NTRS1980

The multispecies modeling of the premixed, laminar steady-state ozone flame

Species dependent kinetic, transport and thermodynamic coefficients were employed in a one dimensional model of the premixed, laminar, steady state ozone flame. Convenient expressions for these coefficients are reported. They are based on independent measurements, no arbitrary parameters are used. The governing equations are solved using a relaxation technique and the partial differential equation package, PDECOL. Species and temperature profiles and the burning velocities are found over the range of initial ozone mole fraction of 0.25 to 1.00. The computed burning velocities are no more than 30% greater than the measurements of Streng and Grosses. Comparison with the computed results of Warnatz shows agreement within + or - 12%, even though quite different expressions for some of the kinetic coefficients were used. These differences are most obvious in the atomic oxygen and temperature profiles at an initial ozone mole fraction of unity.

Heimerl, J. M.↗
NASA NTRS2000

Knowledge Preservation Techniques That Can Facilitate Intergroup Communications

We have developed tools, social methods and software, for (1) acquiring technical knowledge from engineers and scientists, (2) preserving that knowledge, (3) making the totality of our stored knowledge rapidly searchable. Our motivation has been, mainly, to preserve rare knowledge of senior engineers who are near retirement. Historical value of such knowledge, and also of our tools, has been pointed out to us by historians. We now propose the application these tools to enhancing communication among groups that are working jointly on a project. Of most value will be projects having groups among whom communication is rare and incomplete. We propose that discussions among members of a group be recorded in audio and that both the actual audio and transcriptions of that audio, and optional other pieces be combined into electronic, webpage-like "books". These books can then be searched rapidly by interested people in other groups. At points of particular interest, a searcher can zoom in on the text and even on the original recordings to pick up nuances (e.g. to distinguish a utterance said in seriousness from one in sarcasm). In this matter, not only can potentially valuable technical details be preserved for the future, but communication be enhanced during the life of a joint undertaking.

Moreman, Douglas↗
NASA NTRS1989

Impaired driving performance in shiftworkers: the role of the circadian system in a multifactorial model

A substantial and growing percentage of the U.S. work force now works on a rotating shift schedule. The repeated changes in sleep-wake, meal and work times inherent in such schedules conflict with the dictates of the internal biological clock and have adverse consequences for the health of the shiftworker population. An important consequence of this conflict is impaired performance, both on and off the job, as indexed by the increased incidence of motor vehicle accidents in shift workers. In this paper we report the results of a survey administered to rotating shift and straight day workers at a manufacturing plant in the eastern U.S. This survey documents an increased incidence of motor vehicle accidents or "near misses" in which sleepiness was implicated as a cause by the respondent. Complaints of poor sleep and increased sleepiness were also significantly more common in shiftworkers than day workers. Last, shiftworkers reported higher caffeine and alcohol consumption, and were more likely to use alcohol as a sleep aid. Although causal links cannot be established using these associative observations alone, previously reported experience with alteration of shift schedules, improvement of levels of alertness, and reduction in adverse performance outcomes corroborate the possibility of a causal link and suggest potential interventions.

NASA Discipline Regulatory Physiology↗