Bugs in digital logic have led to some significant security vulnerabilities. Hardware bugs are particularly troublesome since they cannot be easily patched. Additionally, if the bug is in the root of trust, all trust built upon it can be vulnerable. Traditional testing either require a deep knowledge of the system, creative attack vectors and lots of human interaction. This is not scalable as there are very few engineers that can wear the hat of a designer, a verification engineer, and a cybersecurity expert. Hardware fuzzing is a relatively new research area in dynamic hardware testing. It has proven to be an effective method for discovering bugs, unexpected behaviors, and security vulnerabilities in software. While hardware fuzzing is new to the hardware domain, it has a strong track record in software testing. Fuzzing is a testing technique that randomly mutates the input data to uncover bugs or vulnerabilities in the design. It is especially good at finding corner cases that test engineers can not envision. Another advantage over other dynamic testing techniques is that, if done well, deep knowledge of the design is not required. Additionally, fuzzing scales well. If the system is set up correctly, it can run unsupervised for weeks if necessary. In this work, we propose using hardware fuzzing to improve the input vector generation for an information flow tracking tool. To get reasonable throughput of test vectors, an emulator is targeted as the execution platform. Efficient emulator execution has some specific requirements.
In this project we uncovered structure-function relationships of donor-acceptor co-crystals used to develop next-generation optoelectronic devices. Unraveling the photodynamics of molecular crystalline materials poses many challenges for spectroscopy due to broad, overlapping features representing numerous underlying dynamical processes. This leads researchers to make many assumptions about the dynamics of a system in choosing an appropriate kinetic fitting model. Computationally, electronic structure methods are either prohibitively expensive or underdeveloped for computing the excited state structure of molecular materials, especially states that exhibit charge transfer. Researchers must therefore perform calculations of excited electronic states using truncated models of molecular materials. Here we present a joint experimental-theoretical approach to bridging the gap between the photodynamics of a molecular material and its constituent molecules. We focus our efforts on quantifying the timescales and mechanisms of photoexcitation in donor-acceptor co-crystals and donor-acceptor dimers where the lowest-lying excited state is characterized by charge transfer from the donor to the acceptor. We employ ultrafast UV pump, UV-Vis probe transient absorption spectroscopy to unravel the time-resolved spectroscopic signatures of the photodynamics in both the crystalline material and donor-acceptor dimers in solution. We perform electronic structure and excited state dynamics calculations of the dimers to inform kinetic fitting models and assign the spectral features. The photodynamics of the crystal vs. dimer systems have many similarities, enabling unprecedented insights into the formation and evolution of charge transfer excitons in the crystalline systems.
The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.
The Department of Energy Hydrogen Fuel Cell Technology Office and Wind Energy Technologies Office’s Wind-H2-Green Steel/Ammonia project is an initiative to demonstrate the feasibility and efficacy of GW-scale integrated energy systems. The team designed reference facilities that utilize wind- and solar-produced hydrogen for industrial steel and ammonia production. This novel concept warranted review of safety codes and standards as they apply to the designs and the identification of codes and standards as they apply to the designs and the identification of codes and standards gaps. This report reviews hydrogen production and storage codes and standards using reference design specifications from a Minnesota steel plant. Requirements, recommendations, and exclusions for the system were identified. Observed gaps included non-specific salt cavern storage requirements, electrolyzer capacity beyond regulated ranges, and lack of requirements for iron reduction via hydrogen. This report will aide future project design efforts and may provide a basis for safety reviews in new designs for industrial facilities with hydrogen production integration.
Nitrogen-containing organic molecules play essential roles in nearly every aspect of chemistry. The importance of these species within gas-phase chemistry has increased in recent years, due to their emissions from wildfires, crude biofuels, ammonia combustion, and CO2 capture facilities. However, there is a lack of detailed relevant mechanistic and kinetic studies of N-compounds, resulting in poor representation of these pathways within chemical models. In this report, we detail our exploration of the gas-phase oxidation mechanisms of pyrrole, imidazole, pyrrolidine, methylamine, and dimethylamine, done by employing multiplexed VUV photoionization mass spectrometry coupled with KinBot, a computational tool that automatically explores multi-well potential energy surfaces. Overall, we vastly expanded the known gas-phase oxidation mechanisms and chemical kinetics of these species. This work will improve the accuracy and completeness of atmospheric and combustion chemistry models, ultimately leading to better-informed decision-makers in the energy and environmental sectors.
Sandia National Laboratories has fabricated a Seismic Sensor Temperature Testbed (SSTT) suitable for exposing sensors under test to a range of reasonably stable temperatures while a co-located reference sensor is maintained at room temperature. The testbed has proven sufficiently quiet to allow recording of high-coherence signals from regional earthquakes in the passband of the seismometers, allowing a direct comparison of signals between the sensors under test and the reference sensor.
The Sea Change Project, managed and financed by SWITCH Maritime, produced the first zero-emission 100% hydrogen fuel-cell powered commercial ferry in the world. The MV Sea Change is a 70-foot catamaran equipped with a 360-kW fuel cell powertrain and a 250-bar 246 kg capacity hydrogen storage system. The 75-passenger vessel began public passenger service in July 2024 as part of the San Francisco Bay Ferry system. Summarized here are aspects of the design of the vessel, its construction, and vessel bunkering with high-pressure hydrogen. Results from a fuel cell performance analysis are also presented, along with estimates of the greenhouse gas (GHG) and criterial pollutant emissions assuming different sourcings of hydrogen. Lessons learned from the project will be briefly reviewed.
While quantum computing offers the promise of exponential advantages, limited quantum speedups are known, especially for practical applications. To open new avenues for quantum advantages, we propose Unconventional Quantum Advantages for Computation (U-QuACs), with respect to unconventional resources such as space (number of bits or quantum bits of memory required to solve a problem), accuracy of solution, communication, or energy consumption. We focus on space-efficient quantum algorithms, where we seek to design algorithms that solve a problem using much less space than the total size of the input. A natural setting in which space is critical is the streaming model of computation, where the input data arrives sequentially in pieces that must each be processed individually. Streaming is motivated by a variety of problems including analysis of internet traffic or social networks. We design the first exponential quantum space advantage for a natural streaming problem, which also constitutes the first quantum advantage for approximating a discrete optimization problem, albeit with respect to space.
In computer science, Document Summarization is the task of condensing some quantity of text and related content through automated means. In this document, we review recent literature in text summarization. “Hybrid” extractive-abstractive approaches continue to be explored. Some of the latest efforts have also sought to enable users to adjust summaries with queries or other structure and begun to test reinforcement-learning style agentic LLM-based solutions.
MACCS is used by the Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This user guide is intended to assist analysts in understanding the MACCS/MACCS-UI User Interface (UI) model and to provide information regarding the code. This user guide version describes MACCS Version 5.0, model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.
Modern computing systems are capable of exascale calculations, which are revolutionizing the development and application of high-fidelity numerical models in computational science and engineering. While these systems continue to grow in processing power, the available system memory has not increased commensurately, and electrical power consumption continues to grow. A predominant approach to limit the memory usage in large-scale applications is to exploit the abundant processing power and continually recompute many low-level simulation quantities, rather than storing them. However, this approach can adversely impact the throughput of the simulation and diminish the benefits of modern computing architectures. We present three novel contributions to reduce the memory burden while maintaining, and sometimes improving, performance in simulations based on finite element discretizations. The first contribution develops dictionary-based data compression schemes that detect and exploit the structure of the discretization, due to redundancies across the finite element mesh. While these schemes are shown to reduce memory requirements by more than 99% on meshes with large numbers of identical mesh cells, there are applications where this structure does not exist. The second contribution leverages a recently developed augmented Lagrangian optimization algorithm to enable r-adaptivity for meshes with the goal of enhancing the redundancies in the mesh. The third contribution extends these methods to patch-based linear solvers and preconditioners by compressing local matrices. Numerical results demonstrate the effectiveness of the proposed methods to detect, enhance and exploit mesh structure on a suite of examples inspired by large-scale applications.
The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.
This report summarizes the findings of a four months FY24 Advanced Science & Technology (AS&T) LDRD Quick Targeted Investigation (QTI) project focused on the exploration of supervised dimension reduction approaches based on autoencoders. Autoencoders have been extensively employed in literature for unsupervised learning tasks, however, their use for supervised regression tasks, which are common within scientific applications, has been limited. Motivated by linear dimension reduction strategies like Active Subspaces and Adaptive Basis, we explored the possibility of employing autoencoders to discover a non-linear manifold able to represent the original function in fewer dimensions. In this report, we discuss a neural network architecture and we perform a numerical campaign on several problems ranging from simple two-dimensional functions to a model problem for magnetohydrodynamics in five dimensions. In our preliminary results, we show that the proposed approach is found to be superior to linear dimension reduction strategies in representing the target function even with a single latent variable.
