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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Expert System for Building TRU Waste Payloads - 13554

The process for grouping TRU waste drums into payloads for shipment to the Waste Isolation Pilot Plant (WIPP) for disposal is a very complex process. Transportation and regulatory requirements must be met, along with striving for the goals of shipment efficiency: maximize the number of waste drums in a shipment and minimize the use of empty drums which take up precious underground storage space. The restrictions on payloads range from weight restrictions, to limitations on flammable gas in the headspace, to minimum TRU alpha activity concentration requirements. The Overpack and Payload Assistant Tool (OPAT) has been developed as a mixed-initiative intelligent system within the WIPP Waste Data System (WDS) to guide the construction of multiple acceptable payloads. OPAT saves the user time while at the same time maximizes the efficiency of shipments for the given drum population. The tool provides the user with the flexibility to tune critical factors that guide OPAT's operation based on real-time feedback concerning the results of the execution. This feedback complements the user's external knowledge of the drum population (such as location of drums, known challenges, internal shipment goals). This work demonstrates how software can be utilized to complement the unique domain knowledge of the users. The mixed-initiative approach combines the insight and intuition of the human expert with the proficiency of automated computational algorithms. The result is the ability to thoroughly and efficiently explore the search space of possible solutions and derive the best waste management decision. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Development of economically viable, highly integrated, highly modular SEGIS architecture

Initiated in 2008, the SEGIS initiative is a partnership involving the U.S. DOE, Sandia National Laboratories, private sector companies, electric utilities, and universities. Projects supported under the initiative have focused on the complete-system development of solar technologies, with the dual goal of expanding renewable PV applications and addressing new challenges of connecting large-scale solar installations in higher penetrations to the electric grid. Petra Solar, Inc., a New Jersey-based company, received SEGIS funds to develop solutions to two of these key challenges: integrating increasing quantities of solar resources into the grid without compromising (and likely improving) power quality and reliability, and moving the design from a concept of intelligent system controls to successful commercialization. The resulting state-of-the art technology now includes a distributed photovoltaic (PV) architecture comprising AC modules that not only feed directly into the electrical grid at distribution levels but are equipped with new functions that improve voltage stability and thus enhance overall grid stability. This integrated PV system technology, known as SunWave, has applications for 'Power on a Pole,' and comes with a suite of technical capabilities, including advanced inverter and system controls, micro-inverters (capable of operating at both the 120V and 240V levels), communication system, network management system, and semiconductor integration. Collectively, these components are poised to reduce total system cost, increase the system's overall value and help mitigate the challenges of solar intermittency. Designed to be strategically located near point of load, the new SunWave technology is suitable for integration directly into the electrical grid but is also suitable for emerging microgrid applications. SunWave was showcased as part of a SEGIS Demonstration Conference at Pepco Holdings, Inc., on September 29, 2011, and is presently undergoing further field testing as a prelude to improved and expanded commercialization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Computing single step operators of logic programming in radial basis function neural networks

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz, with a pulse of about 0.7μs. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage because of cavitation. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. Therefore, the 2 MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop a gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) was performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop under varying lighting conditions. Initially, methods such as circular Hough transforms were applied after a series of images processing to obtain a general distribution of bubble sizes. Bubbles smaller than 500 μm are preferred to effectively mitigate the effect of pressure waves, which demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pretrained convolutional neural network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.

Rasheed, Fayaz↗
DOE OSTI2013

Fabrication of vertically aligned ferroelectric polyvinylidene fluoride mesoscale rod arrays

Here, we have fabricated vertically aligned ferroelectric PVDF mesoscale rod arrays comprising and phases using a 200 nm diameter anodized aluminum oxide (AAO) as the porous template. We could synthesize the ferroelectric phase in mesoscale rod forms by combining the well-established recipe for crystallizing the phase using dimethyl sulfoxide (DMSO) at low temperature and template-guided infiltration processing for the rods using AAO. We also measured the dimensions of the PVDF rods by scanning electron microscopy and identified the polymorph phases by X-ray diffraction and Fourier transform infrared spectroscopy. The length of the rods varied from 3.82 m to 1.09 m and the diameter from 232 nm to 287 nm when the volume ratio between DMSO and acetone changed from 5 : 5 to 10 : 0. We obtained well-defined piezoresponse hysteresis loops for all rods with remnant piezoresponse ranging from 2.12 pm/V to 5.04 pm/V and coercive voltage ranging from 2.29 V to 2.71 V using piezoresponse force microscopy. These results serve as a processing platform for flexible electronic devices that need high capacitance and piezoelectric functionalities such as flexible memory devices or body energy harvesting devices for intelligent systems. (c) 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 3842-3848, 2013

36 MATERIALS SCIENCE↗
DOE OSTI2017

Sparse Representations to Replace TOFD Images in Non-destructive Testing of Materials

This paper describes a proposed method for the selection of relevant samples of ultrasonic signals during automatic material inspection. Instead of the well-known time of flight diffraction (TOFD) images, data are stored as a sparse matrix in which the elements only indicate whether a defect has been detected. This technique avoids storage of useless signals received during probe displacement in cases of low and high signal-to-noise ratios that correspond to coarse-grained and fine-grained materials, respectively. The approach is based on comparing the positions of maximum amplitudes, which are randomly located when signals only consist of noise but are in the same signal range when a defect is detected. The matrix elements are then applied as inputs to a self organizing map by neural networks to produce a normalized sparse matrix as the output, with a constant number of elements. This approach will be beneficial to enable the use of selected data in intelligent systems requiring a fixed number of inputs to characterize defects.

42 ENGINEERING↗
DOE OSTI2019

An efficient hybrid visible light communication/radio frequency system for vehicular applications

This paper focuses on improving the traffic system for Intelligent Transportation System (ITS) by using an imaging receiver instead of photodiode as a single receiver. Here, two simple traffic models have been proposed and analyzed in order to optimize the design characteristics such as signal to noise ratio, required power, received information, bit error rate, and modulation technique. First, a standalone visible light communication (VLC) with different modulation techniques is considered. The On–OFF Keying, L-Pulse Position Modulation (L-PPM), and Inverse L-Pulse Position Modulation (I-L-PPM) are investigated. Then, a hybrid communication system utilizing VLC and radio frequency (RF) is proposed for position-based services. The VLC/RF system combines the benefits of both systems and offers long distance transmission which is an important concern in ITS and in order to improve the link reliability in infrastructure to vehicle communications. This means that the probability of having a communication link between the transmitter and receiver is maintained without interruption. Then, the performance is finally evaluated. The obtained simulation results show a considerable increase in the received information using the proposed hybrid VLC/RF system compared to a VLC system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

The influence of printed electronics on the recyclability of paper: A case study for smart envelopes in courier and postal services

Highlights: • Study of the influence of components of printed electronics in paper recycling. • Comparison between paper recycled with and without resistors, batteries and layouts. • Mechanical and optical properties are evaluated in paper handsheets obtained. • Tensile strength of recycled paper is slighted reduced by layouts. • Optical properties of recycled paper slightly varies with layouts and batteries. - Abstract: The aim of this paper is to analyse the effects of the presence of printed electronics on the paper waste streams and specifically on paper recyclability. The analysis is based on a case study focussed on envelopes for postal and courier services provided with these intelligent systems. The smart printed envelope of the study includes a combination of both conventional (thin flexible batteries and resistors) and printed electronic components (conductive track layout based on nanosilver ink). For this purpose, a comparison between envelopes with and without these components (batteries, resistors and conductive track layouts) was carried out through pilot scale paper recycling tests. The generation of rejects during the recycling process as well as the final quality of the recycled paper (mechanical and optical properties) were tested and quantitatively evaluated. The results show that resistors are retained during the screening process in the sieves and consequently they cannot end up in the final screened pulp. Therefore, mechanical and optical properties of the recycled paper are not affected. Nevertheless, inks from the conductive track layouts and batteries were partially dissolved in the process water. These substances were not totally retained in the sieving systems resulting in slight changes in the optical properties of the final recycled paper (variations are 7.2–7.5% in brightness, 8.5–10.7% in whiteness, 1.2–2.2% in L{sup ∗} values, 3.3–3.5% in opacity and 16.1–27% in yellowness). These variations are not in ranges able to cause problems in current paper recycling processes and restrict the use of recycled paper in current applications. Moreover, real impacts on industrial recycling are expected to be even significantly lower since the proportion of paper product with printed circuits in the current paper waste streams are much lower than the ones tested in this work. However, it should be underlined the fact that this situation may change over the next years due to the future developments in printed electronics and the gradual penetration of these types of devices in the market.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Electronic representation of wave equation

The Taylor series method for solving differential equations represents a non-traditional way of a numerical solution. Even though this method is not much preferred in the literature, experimental calculations done at the Department of Intelligent Systems of the Faculty of Information Technology of TU Brno have verified that the accuracy and stability of the Taylor series method exceeds the currently used algorithms for numerically solving differential equations. This paper deals with solution of Telegraph equation using modelling of a series small pieces of the wire. Corresponding differential equations are solved by the Modern Taylor Series Method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗
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 OSTI2018

167-PFlops Deep Learning for Electron Microscopy: From Learning Physics to Atomic Manipulation

An artificial intelligence system called MENNDL, which used 25,200 Nvidia Volta GPUs on Oak Ridge National Laboratory’s Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity toward nanofabricating materials automatically. Finally, MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road Condition Monitoring System

A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Skin-like low-noise elastomeric organic photodiodes

Stretchable optoelectronics made of elastomeric semiconductors could enable the integration of intelligent systems with soft materials, such as those of the biological world. Organic semiconductors and photodiodes have been engineered to be elastomeric; however, for photodetector applications, it remains a challenge to identify an elastomeric bulk heterojunction (e-BHJ) photoactive layer that combines a low Young’s modulus and a high strain at break that yields organic photodiodes with low electronic noise values and high photodetector performance. Here, a blend of an elastomer, a donor-like polymer, and an acceptor-like molecule yields a skin-like e-BHJ with a Young’s modulus of a few megapascals, comparable to values of human tissues, and a high strain at break of 189%. Elastomeric organic photodiodes based on e-BHJ photoactive layers maintain low electronic noise current values in the tens of femtoamperes range and noise equivalent power values in the tens of picowatts range under at least 60% strain.

42 ENGINEERING↗
DOE OSTI2025

BOSC 2025, the 26th Bioinformatics Open Source Conference

The 26th annual Bioinformatics Open Source Conference (BOSC 2025, open-bio.org/events/bosc-2025) brought its community-driven focus on open-source bioinformatics and open science to the 2025 conference on Intelligent Systems for Molecular Biology and the European Conference on Computational Biology (ISMB/ECCB 2025). Since its launch in 2000, BOSC has been the premier annual meeting covering open-source bioinformatics and open science. Framed by two keynote addresses and a thought-provoking panel discussion, the two-day conference included sessions dedicated to open data, analytic tools and pipelines, workflow platforms, knowledge representation, and the application of AI/ML. The first keynote talk was delivered by Christine Orengo: “Working together to develop, promote and protect our data resources: Lessons learnt developing CATH and TED.” A joint session with the Bio-Ontologies and Knowledge Representation (BOKR) track the second day of BOSC started with a keynote talk by Chris Mungall entitled “Open Knowledge Bases in the Age of Generative AI”. A closing panel on Data Sustainability, moderated by Mónica Muñoz Torres, featured panelists Scott Edmunds, Varsha Khodiyar, Tony Burdett, Nicky Mulder, and Chris Mungall. This year, the CollaborationFest collaborative work event that typically precedes or follows ISMB was incorporated as part of the main conference and organized by BOSC with help from the Function and 3D-SIG tracks.

bioinformatics↗
DOE OSTI2019

BOSC 2019, the 20th annual Bioinformatics Open Source Conference

The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on “Building infrastructure for responsible open science in Africa”. Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The 2015 Bioinformatics Open Source Conference (BOSC 2015)

The Bioinformatics Open Source Conference (BOSC) is organized by the Open Bioinformatics Foundation (OBF), a nonprofit group dedicated to promoting the practice and philosophy of open source software development and open science within the biological research community. Since its inception in 2000, BOSC has provided bioinformatics developers with a forum for communicating the results of their latest efforts to the wider research community. BOSC offers a focused environment for developers and users to interact and share ideas about standards; software development practices; practical techniques for solving bioinformatics problems; and approaches that promote open science and sharing of data, results, and software. BOSC is run as a two-day special interest group (SIG) before the annual Intelligent Systems in Molecular Biology (ISMB) conference. BOSC 2015 took place in Dublin, Ireland, and was attended by over 125 people, about half of whom were first-time attendees. Session topics included “Data Science;” “Standards and Interoperability;” “Open Science and Reproducibility;” “Translational Bioinformatics;” “Visualization;” and “Bioinformatics Open Source Project Updates”. In addition to two keynote talks and dozens of shorter talks chosen from submitted abstracts, BOSC 2015 included a panel, titled “Open Source, Open Door: Increasing Diversity in the Bioinformatics Open Source Community,” that provided an opportunity for open discussion about ways to increase the diversity of participants in BOSC in particular, and in open source bioinformatics in general. The complete program of BOSC 2015 is available online at http://www.open-bio.org/wiki/BOSC_2015_Schedule.

59 BASIC BIOLOGICAL SCIENCES↗