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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Novel Quantum Dot Technology Shows Promise in Simplifying Vehicle and Structural Surface Inspections

A team of researchers from both the Air Force Institute and Technology and Los Alamos National Laboratory are developing a paint that contains a material called quantum dots which can optically measure deformation of a surface. Quantum dots are the same materials used in new QLED television displays; however, they can also be used for a multitude of sensing applications such as deformation. The researchers used a new method of applying an interfacial layer between the quantum dot paint and the surface under investigation to help adhesion in harsh environments. The primary motivation behind this research is to make both quality assurance and maintenance of aircraft easier for the warfighter. The researchers’ efforts started in 2019 and are continuing. The technology works by shining a light onto the surface of the paint and the dots will emit back a wavelength of light that relates to the amount of deformation the surface is going through. This has the promise to allow for real-time 2D surface measurements that is more affordable than other 2D surface measurement alternatives.

42 ENGINEERING↗
DOE OSTI2020

Comparative Study of Differentially Private Data Synthesis Methods

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders’ behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. Here, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Catalytic Approaches to C–O Activation in Biorenewable Feedstocks

The carbon present in biomass (the largest renewable source on the planet) continues to tempt us as a possible replacement for the carbon we currently obtain from fossil fuels. As we have recently reviewed, the challenges of extracting chemically useful feedstocks from biorenewables, and, especially, cellulosics, is largely under-developed relative to the maturity of the technologies for deriving industrially useful chemicals from petroleum. This ultimately boils down to problems in functionalization catalysis (for oil) versus defunctionalization (for cellulosics). The technology gap for achieving the selective defunctionalization of cellulosic feedstocks is much larger than it is for petroleum functionalization. This assessment suggests that the problem is not that bio-renewable feedstocks are fundamentally flawed from an economic and chemical sustainability perspective, but that we lack the technologies for deoxygenating cellulosic to more valuable chemicals. Bozell and Petersen have argued that integrated biorefineries will require a suite of products for viability (in analogy to an integrated petroleum refinery). These products will range from low-value high-volume energy related products (i.e. fuels) to high-value low-volume compounds focused on specialty markets and pharmaceuticals. The latter, although less impactful in terms of volume, is a critical economic component of the future integrated biorefinery. Our review was stimulated by their assessment of the state of the biorefinery and focused on the available chemistries for achieving low-volume high-value outcomes. To help fill this technology gap, we seek new catalytic technologies for synthesizing high-value chemicals from cellulosics and take the view that the inherent chirality of C 6 O 6 feedstocks will provide valuable materials for specialty and pharmaceutical applications, but only if one can achieve site-selective reactions that don’t denude the stereochemistry inherent to the sugars. Eliminative methods are less desirable as this deoxygenation scheme removes multiple stereocenters. The research plan outlined herein seeks methods for site-selective deoxygenation reactions that lead to high value specialty/pharma products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Approach to Dependence Assessment in Human Reliability Analysis: Application of Lag and Linger Effects

Dependence assessment refers to an approach used in human reliability analysis (HRA) to adjust a human error probability (HEP) for the following action by considering the impact of the preceding action. It has been known to significantly affect the overall results of probabilistic safety assessment (PSA). If the dependence assessment is not adequate, the result could be unconvincing for explaining the operator failures in the context of PSA. To date, several methods and some recent research have identified suggestions for treating dependence issues in HRA; however, these are still exclusively based on the intrinsic approach of the Technique for Human Error Rate Prediction (THERP), an HRA method. THERP inevitably has a challenge with the subjectivity of expert evaluation as well as the requirement for PSA and HRA expertise with resource-intensive and time-consuming processes. This paper suggests an approach to dependence assessment that could not only minimize the influence of expert judgment, but also saves time to perform the analysis with reasonable manpower. It modifies existing HRA methods with considering lag and linger effects to apply dependence effects for them. Based on a representative HRA method, i.e., Standardized Plant Analysis Risk - HRA (SPAR-H), guidance for how to apply lag and linger effects for the HRA method is suggested. Then, an investigation is carried out to compare quantification results of the revised HRA method with that of the original approach based on experimental data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

Although deep learning has made great progress in recent years, the exploding economic and environmental costs of training neural networks are becoming unsustainable. To address this problem, there has been a great deal of research on algorithmically-efficient deep learning, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program. In this paper, we present a structured and comprehensive overview of the research in this field. First, we formalize the algorithmic speedup problem, then we use fundamental building blocks of algorithmically efficient training to develop a taxonomy. Our taxonomy highlights commonalities of seemingly disparate methods and reveals current research gaps. Next, we present evaluation best practices to enable comprehensive, fair, and reliable comparisons of speedup techniques. To further aid research and applications, we discuss common bottlenecks in the training pipeline (illustrated via experiments) and offer taxonomic mitigation strategies for them. Finally, we highlight some unsolved research challenges and present promising future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Simulations of neutron noise in the research reactor AKR-2: comparison between a discrete ordinates and a diffusion-based method

A diffusion-based and a discrete ordinates method are used to simulate a neutron noise experiment in the research reactor AKR-2 at the Technical University in Dresden, Germany. The AKR-2 reactor provides an interesting case for the comparison between the two methods because it is characterized by large heterogeneities and regions with low macroscopic neutron cross-sections. For the calculations, the same spatial discretization and the same set of two-energy macroscopic neutron cross-sections with isotropic scattering are used. Significant discrepancies between the diffusion-based and discrete ordinates methods are found in regions of the systems where the diffusion approximation is expected to be inaccurate in reproducing characteristics of the static neutron flux and neutron noise. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2024

Biomass Attributes and Attribute Modifications Affecting Systems and Methods to Separate and Fractionate. In: Handbook of Biorefinery Research and Technology

Chemical and physical heterogeneity in biomass feedstocks such as agricultural or forestry residues is due to substantial differences in plant tissue types. These differences can contribute significant challenges to handling, preprocessing, and conversion in biorefining processes. An understanding of this chemical and physical heterogeneity can be used to inform fractionation technologies that could facilitate more streamlined processing and potentially be employed to yield multiple co-product streams for a single feedstock. In this chapter, the motivation and scope of biomass fractionation is first outlined. Physical and chemical properties of biomass feedstocks, along with their distribution and diversity within plants is next discussed with respect to how these differences can be exploited in a fractionation process. A summary of some of the key physical principles that allow for fractionation is next covered along with how these physical principles are exploited in equipment designs. Examples from the literature are briefly discussed that highlight how these approaches can be employed to achieve processing objectives. Several case studies on physical fractionation of corn stover and forestry residues are presented that illustrate how integrated fractionation processes could be employed. Finally, prospects and potential economic drivers for adoption of biomass fractionation technologies are discussed.

Biomass chemical properties↗
DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Cyber Risk Considerations for Nuclear Digital I&C Systems

Many engineers and scientists researching modeling and analysis methods for nuclear energy advancement are focused on proof-of-concept and early-stage development for new reactor designs. Since instrumentation and control (I&C) systems are traditionally developed later in the systems engineering lifecycle, researchers may be unfamiliar with modern digital technology used in these I&C systems. It is even more likely that they are unfamiliar with the cyber risks associated with them. This paper provides a brief overview of nuclear digital I&C systems before examining the importance of cyber risk management at nuclear reactors and the benefits of including Cyber-Informed Engineering throughout the systems engineering lifecycle. The primary goal is for engineers and scientists to develop a mindset that incorporates cybersecurity as another discipline alongside safety when designing and developing new technologies, regardless of technology readiness level.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Technical Specification Surveillance Interval Extension Using Self-Diagnostics

As part of the Light Water Reactor Sustainability program, an ongoing research effort is being conducted on technical specifications surveillance interval extension of digital equipment in nuclear power plants. The research team is led by Idaho National Laboratory and includes Pacific Northwest National Laboratory, Technology Resources, and Oak Ridge National Laboratory. This research focuses on developing methods for applying the U.S. Nuclear Regulatory Commission (NRC)–approved guidance to implement a licensee-controlled, risk-informed surveillance frequency change program in digital instrumentation and control (I&C) systems that include self-diagnostics and online monitoring (OLM) capabilities. Although approved methods exist for extending technical specifications (TS) surveillance test intervals (STIs) for general equipment, including analog I&C equipment, gaps remain in technology and guidance on crediting newer digital equipment’s internal self-diagnostics and OLM characteristics. Previous research described a general methodology for crediting internal self-diagnostics for extending surveillance test intervals. The methodology used self-diagnostics to detect—and credited recovery from—failure. Self-diagnostics were also applied for performance monitoring during the extended surveillance interval. This report discusses the status of recent activities to evaluate the previously developed methodology using a pilot study. Although both a utility partner for a pilot study and a specific digital asset were identified in FY2020, delays in obtaining proprietary information resulted in a limited ability to fully evaluate the methodology, and further interactions were complicated by the COVID pandemic. Therefore, at that time, the use of public-domain information—along with current processes for surveillance interval extension through a surveillance frequency control program—identified the need to fully assess diagnostic coverage as part of the pilot study. Furthermore, self-diagnostics were also identified as a potential option to replace the drift analyses conducted as part of current STI extension procedures. In FY2022, the project was reconstituted with the industry partner, and information and data were made available by the industry partner to the research team for review. The shared information included failure event descriptions and data for a digital I&C system since its implementation, as well as recent STI extension interval reports developed by the utility partner on that digital I&C system. This report presents an evaluation of this information and data and describes an application of the proposed methodology cited above. The methodology seeks to take advantage of the self-diagnostics and OLM capabilities to reduce risk or reduce the level of qualitative monitoring assessment needed to perform a risk-informed STI extension using existing NRC approved guidance or both. Addressing these issues of STI extension by crediting self-diagnostics is likely to result in benefits for current and future nuclear power plant (NPP) operations, including lowering the barriers to adoption of digital I&C systems and increasing cost savings by deferring or eliminating unneeded preventive maintenance (tasks or checks or activities). Specifically, self-diagnostic and OLM capabilities of newer digital equipment being installed in non-safety and safety applications are designed to detect failures, provide early warning of potential failures, and notify plant operators to take appropriate action to reduce out of service (OOS) time thus protecting safety margins. Moreover, the equipment is expected to provide information that time-related operational degradation is identified early to ensure timely and planned corrective actions instead of a reactive and unplanned approach ahead of an extended-surveillance interval.

42 ENGINEERING↗
DOE OSTI2024

Relevant biochar characteristics influencing compressive strength of biochar-cement mortars

To counteract the contribution of CO 2 emissions by cement production and utilization, biochar is being harnessed as a carbon-negative additive in concrete. Increasing the cement replacement and biochar dosage will increase the carbon offset, but there is large variability in methods being used and many researchers report strength decreases at cement replacements beyond 5%. This work presents a reliable method to replace 10% of the cement mass with a vast selection of biochars without decreasing ultimate compressive strength, and in many cases significantly improving it. By carefully quantifying the physical and chemical properties of each biochar used, machine learning algorithms were used to elucidate the three most influential biochar characteristics that control mortar strength: initial saturation percentage, oxygen-to-carbon ratio, and soluble silicon. These results provide additional research avenues for utilizing several potential biomass waste streams to increase the biochar dosage in cement mixes without decreasing mechanical properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Business Case Evaluation of Gas Switching Reforming (GSR) Technology: A Promising Technology for Natural Gas Reforming with Integrated CO2 Capture

Hydrogen is essential in the transition to sustainable energy, and developing low-carbon production methods is a key research focus. Traditional steam methane reforming (SMR) dominates the hydrogen industry but contributes substantially to CO2 emissions. In response, Gas Switching Reforming (GSR) has emerged as a novel process that integrates carbon capture and utilizes process heat more efficiently. Unlike other reforming methods, GSR consolidates oxidation and reduction reactions within a single reactor, which minimizes external energy inputs and simplifies scaling. Like conventional steam methane reforming (SMR), GSR can be integrated with water-gas shift and pressure swing adsorption units for pure hydrogen production. This work presents a comprehensive business case analysis of GSR technology based on experimental results in Technology Readiness Level 3, Life Cycle Assessment (LCA) and Techno-Economic (TEA) evaluation incorporating ASPEN Plus process modeling considering different configurations and energy scenarios. The TEA incorporates data from kinetic experiments from various catalysts to evaluate the GSR process under various conditions. The goal of this work is to evaluate GSR’s potential to serve as a low-carbon alternative to SMR, focusing on global warming potential and additional impact categories to evaluate a wide spectrum of environmental impacts. Comparative assessments were conducted with SMR, chemical loop reforming (CLR), and proton exchange membrane (PEM) electrolysis to explore trade-offs across environmental metrics. The environmental impact assessment of this work encompasses the entire hydrogen production lifecycle from raw material extraction to plant decommissioning, using a cradle-to-gate boundary. Preliminary findings highlight that GSR, when integrated with low-carbon energy sources, could significantly reduce environmental impacts, making it a promising candidate for low-carbon hydrogen infrastructure. The insights from this business case evaluation aim to guide industry in scale-up and commercialization of this promising clean energy technology.

03 NATURAL GAS↗
DOE OSTI2024

Recent Advances in Immobilizing and Benchmarking Molecular Catalysts for Artificial Photosynthesis

Transition metal complexes have been widely used as catalysts or chromophores in artificial photosynthesis. Traditionally, they are employed in homogeneous settings. Despite their functional versatility and structural tunability, broad industrial applications of these catalysts are impeded by the limitations of homogeneous catalysis such as poor catalyst recyclability, solvent constraints (mostly organic solvents), and catalyst durability. Over the past few decades, researchers have developed various methods for molecular catalyst heterogenization to overcome these limitations. Here, in this review, we summarize recent developments in heterogenization strategies, with a focus on describing methods employed in the heterogenization process and their effects on catalytic performances. Alongside the in-depth discussion of heterogenization strategies, this review aims to provide a concise overview of the key metrics associated with heterogenized systems. We hope this review will aid researchers who are new to this research field in gaining a better understanding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Discovering Ca II absorption lines with a neural network

Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in the early universe. Calcium II (Ca II ) absorbers, which are one of the dustiest absorbers and are located at lower redshifts than most other absorbers, are especially valuable when studying physical processes and conditions in recent galaxies. However, the number of known quasar Ca II absorbers is relatively low due to the difficulty of detecting them with traditional methods. In this work, we developed an accurate and quick approach to search for Ca II absorption lines using deep learning. In our deep learning model, a convolutional neural network, tuned using simulated data, is used for the classification task. The simulated training data are generated by inserting artificial Ca II absorption lines into original quasar spectra from the Sloan Digital Sky Survey (SDSS), while an existing Ca II catalogue is adopted as the test set. The resulting model achieves an accuracy of 96 per cent on the real data in the test set. Our solution runs thousands of times faster than traditional methods, taking a fraction of a second to analyse thousands of quasars, while traditional methods may take days to weeks. The trained neural network is applied to quasar spectra from SDSS’s DR7 and DR12 and discovered 399 new quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II absorbers identified previously by other research groups through traditional methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗
DOE OSTI2024

Innovative Joining Method for Hybrid Composites with Tailored Performance

In various industries, such as renewables and aerospace, the demand for carbon fiber is rapidly growing to meet the requirements of high-performance applications with optimized designs. Traditionally, these optimized designs have been limited to single-material composites due to process constraints. However, this study aims to explore the feasibility of transitioning between two different fiber types for pultrusion and filament winding processes, thereby enhancing design flexibility, and optimizing performance. This research introduces a novel method of hybrid reinforcement through splicing techniques. By employing a splicing method, fibers are merged prior to infusion, resulting in a transition of higher strength compared to traditional joints. Moreover, this technique enables the creation of composites with variable compositions, allowing for the selective placement of properties in a process previously characterized by static properties. The objective of this paper is to evaluate the mechanical properties of dry fiber splicing and its impact on the resulting composite material. Through comprehensive analysis, we aim to provide insights into the feasibility and advantages of employing this innovative splicing technique in composite manufacturing processes.

Guzorek, Steven↗