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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of machine learning (ML), specifically neural networks, has grown significantly and has spurred research in its applicability to digital instrumentation and control systems (DI&C). While ML models have shown promise in operational contexts, the trustworthiness of using such algorithms has not been adequately assessed. Failures of ML integrated systems are not well understood, and the lack of comprehensive risk modeling can degrade the trustworthiness in these systems. In recent reports by the National Institute for Standards and Technology (NIST) [1] and the Nuclear Regulatory Commission (NRC) [2], they indicate that trustworthiness in ML is a critical barrier and will play a vital role in the safe, accountable, and secure operation of intelligent systems. Thus, in this work, we demonstrate a dynamic model-agnostic method to quantify the relative reliability of AI/ML predictions by incorporating out-of-distribution (OOD) detection on the training dataset. It is well documented that most ML algorithms excel at interpolation (or near-interpolation) tasks but experience significant performance degradation at extrapolation. The method, referenced as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets which can used to the relative reliability of AI/ML predictions. LADDR is then demonstrated on a feedforward neural network based digital twin used for the prediction of safety significant factors during a loss-of-flow transient. LADDR is used to demonstrate how training data can be used as evidence to support the relative reliability of ML/AI predictions enhancing the overall trustworthiness of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Universal Utility Data Exchange (UUDEX) – Security and Administration: Cybersecurity of Energy Delivery Systems (CEDS) Research and Development

A critical component of the Universal Utility Data Exchange (UUDEX) approach is the integrated security contained within its processing. This document describes how that security is designed and expected to be implemented by UUDEX Implementations (U-Implementations), including the UUDEX Server (U-Server) and UUDEX Clients (U-Clients). The UUDEX security hierarchy consists of three levels: 1. The UUDEX Instance (U-Instance) itself, which sits at the top of the hierarchy and contains the U-Server, the UUDEX Identity Authority (U-Identity Authority), and the UUDEX Administrator (U-Administrator) functions; 2. A group of one or more UUDEX Participants (U-Participants) that present “organizations” that participate in the U-Instance and contains the UUDEX Administrator Participant (U-U-Administrator Participant) function; 3. A group of one or more UUDEX Endpoints (U-Endpoints) that represent the individual UUDEX Publish Clients (U-Publish Client) responsible for supplying data to the U-Instance that is consumed by UUDEX Subscriber Clients (U-Subscriber Clients). U-Endpoints can be either autonomous devices that publish and subscribe data such as data exchange servers found in supervisory control and data acquisition and energy management systems, or they can be tied to users of applications that, for example, submit DOE OE-417 disturbance reports. U-Participants and U-Endpoints can be organized into UUDEX Groups (U-Groups). Any number of U-Participants or U-Endpoints can be members of a U-Group. A given U-Participant or U-Endpoint can be a member of multiple U-Groups, but a U-Group cannot contain other U-Groups. For example, a U-Group could be created to contain all U-Participant Transmission Operators within the purview of a Reliability Coordinator, and another U-Group could be created to contain all U-Participant Generator Operators within the purview of a Reliability Coordinator. U-Participants that are both Transmission Operators and Generator Operators would be members of both U-Groups. U-Participants, U-Endpoints, and U-Groups are used in the access control structures to provide access to individual UUDEX Subjects (U-Subjects). U-Groups are created by the U-Administrator and are managed by the U-Administrator or the designated U-Group Managers. U-Endpoints can be assigned UUDEX Roles (U-Roles) that can be used to further restrict access. U-Roles are assigned to individual U-Endpoints. For example, a U-Role of “Security Analyst” could be used to restrict which U-Endpoints can publish or subscribe security incident reports and vulnerability notifications, while a U-Role of “Transmission Planner” can be used to restrict which U-Endpoints can publish power system model updates. U-Role definitions are created by the U-Administrator, but the U-Roles are assigned to U-Endpoints by their respective UUDEX Participant Administrators (U-Participant Administrator). Because all information required to make security decisions is either included within the U-Endpoint’s X.509 digital certificate or stored in a datastore on the U-Server, all security decisions are performed and enforced within the U-Server. This reduces the complexity of the U-Client code and minimizes the chance for compromise of the integrity of the UUDEX security features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Resilient State Recovery Using Prior Measurement Support Information

Resilient state recovery of cyber-physical systems has attracted much research attention due to the unique challenges posed by the tight coupling between communication, computation, and the underlying physics of such systems. By modeling attacks as additive adversary signals to a sparse subset of measurements, this resilient recovery problem can be formulated as an error correction problem. To achieve exact state recovery, most existing results require less than 50% of the measurement nodes to be compromised, which limits the resiliency of the estimators. In this paper, we show that observer resiliency can be further improved by incorporating data-driven prior information. Here, we provide an analytical bridge between the precision of prior information and the resiliency of the estimator. By quantifying the relationship between the estimation error of the weighted ℓ 1 observer and the precision of the support prior, this quantified relationship provides guidance for the estimator’s weight design to achieve optimal resiliency. Several numerical simulations and an application case study are presented to validate the theoretical claims.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2014

Mars Sample Return Using Commercial Capabilities: Mission Architecture Overview

Mars Sample Return (MSR) is the highest priority science mission for the next decade as recommended by the recent Decadal Survey of Planetary Science. This paper presents an overview of a feasibility study for an MSR mission. The objective of the study was to determine whether emerging commercial capabilities can be used to reduce the number of mission systems and launches required to return the samples, with the goal of reducing mission cost. We report the feasibility of a complete and closed MSR mission design using the following scenario that covers three synodic launch opportunities, beginning with the 2022 opportunity: A Falcon Heavy injects a SpaceX Red Dragon capsule and trunk onto a Trans Mars Injection (TMI) trajectory. The capsule is modified to carry all the hardware needed to return samples collected on Mars including a Mars Ascent Vehicle (MAV), an Earth Return Vehicle (ERV), and hardware to transfer a sample collected in a previously landed rover mission to the ERV. The Red Dragon descends to land on the surface of Mars using Super Sonic Retro Propulsion (SSRP). After previously collected samples are transferred to the ERV, the single-stage MAV launches the ERV from the surface of Mars. The MAV uses a storable liquid bi-propellant propulsion system to deliver the ERV to a Mars phasing orbit. After a brief phasing period, the ERV, which also uses a storable bi-propellant system, performs a Trans Earth Injection (TEI) burn. Upon arrival at Earth, the ERV performs Earth and lunar swing-bys and is placed into a lunar trailing circular orbit - an Earth orbit, at lunar distance. A later mission, using Dragon and launched by a Falcon Heavy, performs a rendezvous with the ERV in the lunar trailing orbit, retrieves the sample container and breaks the chain of contact with Mars by transferring the sample into a sterile and secure container. With the sample contained, the retrieving spacecraft makes a controlled Earth re-entry preventing any unintended release of pristine martian materials into the Earth's biosphere. The analysis methods employed standard and specialized aerospace engineering tools. Mission system elements were analyzed with either direct techniques or by using parametric mass estimating relationships (MERs). The architecture was iterated until overall mission convergence was achieved on at least one path. Subsystems analyzed in this study include support structures, power system, nose fairing, thermal insulation, actuation devices, MAV exhaust venting, and GN&C. Best practice application of loads, mass growth contingencies, and resource margins were used. For Falcon Heavy capabilities and Dragon subsystems we utilized publically available data from SpaceX, published analyses from other sources, as well as our own engineering and aerodynamic estimates. Earth Launch mass is under 11 mt, which is within the estimated capability of a Falcon Heavy, with margin. Total entry masses between 7 and 10 mt were considered with closure occurring between 9 and 10 mt. Propellant mass fractions for each major phase of the EDL - Entry, Terminal Descent, and Hazard Avoidance - have been derived. An assessment of the effect of the entry conditions on the thermal protection system (TPS), currently in use for Dragon missions, shows no significant stressors. A useful payload mass of 2.0 mt is provided and includes mass growth allowances for the MAV, the ERV, and mission unique equipment. We also report options for the MAV and ERV, including propulsion systems, crewed versus robotic retrieval mission, as well as direct Earth entry. International planetary protection policies as well as verifiable means of compliance will have a large impact on any MSR mission design. We identify areas within our architecture where such impacts occur. We also describe preliminary compliance measures that will be the subject of future work. This work shows that emerging commercial capabilities as well as new methodologies can be used to efficiently support an important planetary science objective. The work also has applications for human exploration missions that use propulsive EDL techniques

Red Dragon↗
DOE OSTI2021

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗
NASA NTRS2005

A Reliable Service-Oriented Architecture for NASA's Mars Exploration Rover Mission

The Collaborative Information Portal (CIP) was enterprise software developed jointly by the NASA Ames Research Center and the Jet Propulsion Laboratory (JPL) for NASA's highly successful Mars Exploration Rover (MER) mission. Both MER and CIP have performed far beyond their original expectations. Mission managers and engineers ran CIP inside the mission control room at JPL, and the scientists ran CIP in their laboratories, homes, and offices. All the users connected securely over the Internet. Since the mission ran on Mars time, CIP displayed the current time in various Mars and Earth time zones, and it presented staffing and event schedules with Martian time scales. Users could send and receive broadcast messages, and they could view and download data and image files generated by the rovers' instruments. CIP had a three-tiered, service-oriented architecture (SOA) based on industry standards, including J2EE and web services, and it integrated commercial off-the-shelf software. A user's interactions with the graphical interface of the CIP client application generated web services requests to the CIP middleware. The middleware accessed the back-end data repositories if necessary and returned results for these requests. The client application could make multiple service requests for a single user action and then present a composition of the results. This happened transparently, and many users did not even realize that they were connecting to a server. CIP performed well and was extremely reliable; it attained better than 99% uptime during the course of the mission. In this paper, we present overviews of the MER mission and of CIP. We show how CIP helped to fulfill some of the mission needs and how people used it. We discuss the criteria for choosing its architecture, and we describe how the developers made the software so reliable. CIP's reliability did not come about by chance, but was the result of several key design decisions. We conclude with some of the important lessons we learned form developing, deploying, and supporting the software.

Mak, Ronald↗
NASA NTRS2018

Giving the Public a Perspective into Unmanned Aircraft Systems' Operations

NASA is currently engaged in research to safely enable large-scale commercial applications of small Unmanned Aerial Systems (UAS) in low altitude airspace. This research effort, referred to as UAS Traffic Management (UTM), encompasses the concepts and technologies needed to accommodate the projected demand of UAS operating in the national airspace. One aspect related to the successful implementation of UTM in the future is public acceptance. Transparency will heavily influence this acceptance, and a public portal will provide much of that transparency through ease of access to information about the operations - mainly who, why, and where such operations are taking place. Related concerns to the public are individual privacy, security, and accountability of the operators. Providing the aforementioned information about operations can mitigate these concerns, but a balance will have to be achieved between the need for transparency from the public and the privacy of the operators. The proper balance and the needs of the various UTM stakeholders with regard to information access will be explored through the development and testing of a public portal as part of NASA's Technical Capability 3 (TCL 3) demonstration. Additionally, various approaches to the display of information and user interfaces will be surveyed through the development of a public portal by multiple UTM industry partners across different test sites.

UAS↗
DOE OSTI2025

PROACTIVE Focus Area 4: Structured Decision Metrics Analysis (Final Report)

A structured decision metrics analysis was developed as one of the tasks under PROACTIVE’s Focus Area 4 (FA4) and used structured decision metrics for processes, items, and facilities (PIF) to determine the efficacy of an M&V system in meeting treaty goals and technical objectives. The method starts with the functional decomposition of a “treaty” with the M&V system overlaid on it. A Functional Decomposition Rubric (FDR) for each PIF is used to determine a score ranging from 0 (weak) to 4 (strong) for assurance, security, and burden. The individual scores are combined to provide an overall Verification System Score (VSS) which assesses the overall verification system’s suitability given goals; scores for each step of the process are also given. The outcome of the evaluation is to identify needs or modifications to the enterprise model, verification system, or proposed testbed capabilities. VeriScore was used to evaluate FA4’s Spiral 0 exercise; those results are included in this report. The VeriScore and FDR framework can also be used in a non-treaty environment, as any goal/objective/method structure will work, thus expanding its applicability beyond traditional arms control structures.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2007

Alternate-Fueled Flight: Halophytes, Algae, Bio-, and Synthetic Fuels

Synthetic and biomass fueling are now considered to be near-term aviation alternate fueling. The major impediment is a secure sustainable supply of these fuels at reasonable cost. However, biomass fueling raises major concerns related to uses of common food crops and grasses (some also called "weeds") for processing into aviation fuels. These issues are addressed, and then halophytes and algae are shown to be better suited as sources of aerospace fuels and transportation fueling in general. Some of the history related to alternate fuels use is provided as a guideline for current and planned alternate fuels testing (ground and flight) with emphasis on biofuel blends. It is also noted that lessons learned from terrestrial fueling are applicable to space missions. These materials represent an update and additions to the Workshop on Alternate Fueling Sustainable Supply and Halophyte Summit at Twinsburg, OH, Oct. 17 to 18, 2007 (ref. 1).

Hendricks, R. C.↗
NASA NTRS2012

Alternate-Fueled Flight: Halophytes, Algae, Bio-, and Synthetic Fuels

Synthetic and biomass fueling are now considered to be near-term aviation alternate fueling. The major impediment is a secure sustainable supply of these fuels at reasonable cost. However, biomass fueling raises major concerns related to uses of common food crops and grasses (some also called "weeds") for processing into aviation fuels. These issues are addressed, and then halophytes and algae are shown to be better suited as sources of aerospace fuels and transportation fueling in general. Some of the history related to alternate fuels use is provided as a guideline for current and planned alternate fuels testing (ground and flight) with emphasis on biofuel blends. It is also noted that lessons learned from terrestrial fueling are applicable to space missions. These materials represent an update (to 2009) and additions to the Workshop on Alternate Fueling Sustainable Supply and Halophyte Summit at Twinsburg, Ohio, October 17 to 18, 2007.

Hendricks, R. C.↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

farm crops↗
DOE OSTI2024

Photoneutron Production Using an Electron Linear Accelerator for Applications in Neutron Imaging

Photoneutron production is possible using an electron linear accelerator and a target capable of generating photonuclear reactions. A short pulse neutron source can be useful for neutron imaging dynamic experiments. This study is aimed at the feasibility of photoneutron production using a 20 MeV electron linear accelerator and tungsten and depleted uranium targets of various thicknesses. MCNP6 (Monte Carlo N-Particle) code will be used to develop a computational model to estimate total neutron yield, and this will be verified at the Idaho State University’s Accelerator Center. After verification of the neutron yield and energy spectra, an additional MCNP6 model will be developed to analyze the neutron imaging processes. This study will potentially prove it is possible to conduct multi-mode imaging experiments on the anticipated Scorpius electron linear accelerator at the Nevada National Security Sites.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Corrosion Resistance of an AlCeMg/Stainless-Steel Reactive Bond

A major issue for metal components in many industries is corrosion as it can substantially reduce their lifetime. This issue is especially problematic for materials used in heat exchanger applications. Al–Ce–Mg alloys, which exhibit corrosion resistance and can reactively bond with other metals, may be a viable solution to this problem. This investigation studied the corrosion behavior of Al–2Ce–6Mg (atomic percent)/stainless-steel (SS) reactive bond interfaces after full immersion in nitric, sulfuric, formic, and mixed acid for 267 h. This particular Al–Ce–Mg alloy was chosen due to its good castability. The results of scanning electron microscope characterization showed that reactive bond formations repeatedly occurred throughout the length of the casting in the as-cut samples and that these formations maintained a secure bonding between the alloy and the stainless-steel tubes. Transmission electron microscopy results showed that there was a clear compositional and microstructural transition across the reactive bond. The results of the immersion tests indicated that the nitric, sulfuric, and the mixed acid did not have an observably negative effect on the reactive bond structure. As for the sample exposed to formic acid only, noticable changes were seen in both the microstructural appearance and the elemental profile across the bond, suggesting that oxide formation occurred.

Brechtl, Jamieson [ORNL] (ORCID:0000000217394283)↗
DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗
DOE OSTI2025

Harvesting Subsea Water Motion to Provide Clean Electrical Power

This TEAMER project had three main objectives related to characterizing the performance of a vortex-induced vibration (VIV) marine energy harvesting device called the Whatever Input To Torsion (WITT). The first was to characterize the motion of the WITT device as it vibrates on top of a pipe in a natural tidal flow. The second was to measure how much electricity the WITT device generates when it is deployed on top of a pipe that is secured to the seabed with a lander. The third objective was to measure how much electricity the WITT device generates in the same configuration but with a longer pipe. This TEAMER project succeeded in satisfying the first objective by measuring the motion of the WITT device with an inertial measurement unit (IMU) mounted in the WITT housing. The motion was described by examining changes in the acceleration, pitch, roll, and yaw, and displacement. The frequency of the motion was characterized using spectral analysis. The TEAMER project succeeded in reaching the second objective by measuring the power produced by the WITT. It is reported here as the average and maximum power in hourly intervals. WITT Energy decided to not have us pursue the third objective of testing a longer pipe because the first test was unexpectedly successful at having the system vibrate at the target frequency. A longer pipe would have resulted in a decreased frequency and therefore not have been a useful test. Based on these findings, the decision was made to keep the same pipe length for both tests and improve on other aspects of the test structure that had failed during the first test. The key finding of this TEAMER project is that the WITT is capable of generating electricity in moderate flow speeds when it is in a housing that is attached to a 2.5 m pipe and secured to the seabed by a lander. This report contains the first power values for a VIV instrument that generates electricity from a pendulum swinging at the top of a long pipe. The experiment demonstrated that the vibration frequency of the device varied with the tidal current speed, which changed throughout a given tidal cycle. This meant that the desired peak frequency was not sustained for more than one hour. The success of this TEAMER test provides evidence that continued research in the area of electricity generation from VIV holds promise as a source of marine energy for offshore applications. Future tests should focus on refining the pipe length equations and improving the power take off system of the WITT power electronics. In addition, experiments aimed at expanded testing and evaluation (e.g. longer than one month) are needed to measure the power production at higher tidal current speeds and test the system's durability.

Branch, Ruth A. (ORCID:0000000202356719)↗
DOE OSTI2023

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.

Peng, Hongwu↗
DOE OSTI2021

Battery Lifecycle Framework

The Battery Lifecycle (BLC) Framework is an open-source platform that provides tools to visualize and share battery data from material characterization, cell testing, manufacturing, and field testing through the technology development cycle. BLC has three components: data importers, a front-end for querying the data and creating visualizations, and an application programming interface to provide access to the data from Python. BLC has been deployed for tracking the development of a battery from the lab to a manufacturing line and systems installed in the field and for comparing studies of multiple cells of the same battery chemistry and configuration. The code was developed around Redash, a robust open-source extract-transform-load engine. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4546 O

De Angelis, Valerio↗