SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Documented high-level generic plant designs for various pulp and paper methods in the United States. Included a basic mass and energy balance for each plant. Included operating costs and rates of pollutants where applicable.
Compressing porous carbon electrodes is a common approach to improve flow battery performance, but the resulting impact on electrode structure, fluid dynamics, and cell performance is not well understood. Herein, microtomographic imaging, load cell testing, and flow cell diagnostics are employed to characterize how compression‐induced changes impact pressure drop, polarization, and mass‐transfer scaling. Five different compressions are tested, spanning ranges typically used in literature, for AvCarb 1071 cloth (0%, 9%, 20%, 25%, 32%) and Freudenberg H23 paper (0%, 8%, 12%, 17%, 22%). It is found that the two electrode structures have distinct responses to compression, resulting in differing optimal conditions identified for each material; specifically, the Freudenberg H23 exhibits lower combined ohmic, charge‐transfer, and mass‐transport values at 8% compression, resulting in improved electrochemical performance across all compressive values, as compared to the optimal AvCarb 1071 compression (20%). Overall, Freudenberg H23 exhibits a greater sensitivity to compression with peak electrochemical activity correlating with increased permeability, whereas AvCarb 1071 is insensitive to compressive loads but produces lower electrochemical performance. Herein, the trade‐offs of mechanical robustness on fluid‐dynamic and electrochemical performance between the two electrodes are demonstrated by the aforementioned findings, suggesting each could be used for specific operating environments.
Industrial process heat decarbonization through electrification could contribute significantly to climate change mitigation efforts. In the US industry, thermal processes accounted for more than two-thirds of the total final energy demand in 2021. Cross-cutting electrification technologies like industrial heat pumps are suitable for the process heat supply to several industrial unit operations in a sustainable way while also improving overall energy efficiency. This study employs a bottom-up approach to investigate the techno-enviro-economic potentials of deploying high-temperature and steam-generating heat pumps in US textile, pulp and paper, and automotive sectors in different timeframes. The results show that the annual technical potential energy and CO 2 savings by electrifying heat supply are 310 PJ (or 36% of the projected energy demand) and 28 MtCO 2 (or 71% of the projected CO 2 emissions) in 2050 respectively, however, these incur additional costs in each sector (ranging between 5 and 18 $\$$/GJ). The required heating capacity of industrial heat pumps is estimated at 15 GW, which translates roughly into a market of over 6000 heat pump units and an investment volume of $\$$7 billion in the studied processes. Although there may be individual cost-effective opportunities for electrifying heat supply in specific industrial sites, the overall costs are estimated to be high in the three industrial sectors due to the large disparity between electricity and natural gas prices and low heat source temperatures. To overcome the identified techno-economic barriers, comprehensive action plans for different stakeholders are also given. This study provides novel insights that should inform policymakers’ and executives’ decisions about the electrification of the current and future US industrial heat supply in relevant industrial sectors.
Direct detection experiments for light (sub-GeV) dark matter are making enormous leaps in reaching previously unexplored theory space. The need for accurate characterizations of target responses has led to a growing interplay between particle and condensed matter physics. This white paper summarizes recent progress on direct detection calculations that utilize state-of-the-art numerical tools in condensed matter physics and effective field theory techniques. These new results provide the theoretical framework for interpreting ongoing and planned experiments using electronic and collective excitations, and for optimizing future searches.
Primordial Black Holes (PBHs) are a viable candidate to comprise some or all of the dark matter and provide a unique window into the high-energy physics of the early universe. This white paper discusses the scientific motivation, current status, and future reach of observational searches for PBHs. Future observational facilities supported by DOE, NSF, and NASA will provide unprecedented sensitivity to PBHs. However, devoted analysis pipelines and theoretical modeling are required to fully leverage these novel data. The search for PBHs constitutes a low-cost, high-reward science case with significant impact on the high energy physics community.
A mechanistic understanding of the synergistic effects of enzymes on cellulosic fibers dewatering and fiber bonding is essential for advancing energy-efficiency and lightweight production of paper and packaging materials. This study investigates the impact of varying cellulase and xylanase formulations on equilibrium moisture content (EMC) after pressing and tensile strength of cellulosic fiber webs using a factorial experimental design. Nine custom enzyme formulations were evaluated at controlled dosages ranging from ∼20 to 76 FPU/mL cellulase and ∼500–1130 IU/mL xylanase. Response surface modeling revealed a significant synergy, particularly at 20–40 FPU/mL cellulase combined with ≥ 1000 IU/mL xylanase. Under these conditions, EMC decreased by up to 3.6% compared with the untreated refined control, while tensile index gains of up to 16% were statistically significant for optimized blends (p < 0.05). The interaction between cellulase and xylanase was also significant for the tensile response (p = 0.010). Protein efficiency analysis showed that optimized formulations containing 30–40% less protein outperformed the commercial benchmark. The nonlinear synergy between cellulase and xylanase is attributed to their complementary substrate specificities. Endoglucanase- and β‑glucosidase‑rich cellulases hydrolyze internal β‑1,4‑glycosidic bonds in amorphous cellulose, loosening fiber walls and increasing flexibility, while xylanases target hemicellulose, primarily xylan-rich domains, enhancing porosity and improve cellulase accessibility. Tailoring enzyme formulations at low loadings overcomes traditional trade-offs between strength and dewatering, enabling cost-effective, energy-efficient, low-carbon solutions for sustainable packaging and hygiene products.
Astrophysical neutrinos are excellent probes of astroparticle physics and high-energy physics. With energies far beyond solar, supernovae, atmospheric, and accelerator neutrinos, high-energy and ultra-high-energy neutrinos probe fundamental physics from the TeV scale to the EeV scale and beyond. They are sensitive to physics both within and beyond the Standard Model through their production mechanisms and in their propagation over cosmological distances. They carry unique information about their extreme non-thermal sources by giving insight into regions that are opaque to electromagnetic radiation. Here in this white paper describes the opportunities astrophysical neutrino observations offer for astrophysics and high-energy physics, today and in coming years.
A rapid isotope ratio screening technique for uranium enrichment is demonstrated by utilizing paper spray ionization (PSI) high-resolution mass spectrometry (HRMS). Measurements were conducted using an ambient ionization mass spectrometer coupled to a custom-made JEOL PSI attachment apparatus, with de minimus sample preparation requirements. The current method detection limit for individual isotopes (e.g., 235 U and 238 U) is approximately 50 pg, with subsequent optimization expected to further improve U isotopic sensitivity. The PSI analytical method described herein can support rapid analysis (both in-field and in-lab screening) of isotopes-of-interest, as demonstrated by empirical differentiation of depleted uranium (DU) and low enriched uranium (LEU) analytical aliquots. Furthermore, this analytical workflow holds promise for applications in nuclear forensics, international nuclear safeguards, and nonproliferation missions.
Thermal insulation materials by recycling pulp and paper wastes play an important role in environmental sustainability of green buildings.
This white paper reports on the discussions of the 2018 Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program 'From bound states to the continuum: Connecting bound state calculations with scattering and reaction theory'. One of the biggest and most important frontiers in nuclear theory today is to construct better and stronger bridges between bound state calculations and calculations in the continuum, especially scattering and reaction theory, as well as teasing out the influence of the continuum on states near threshold. This is particularly challenging as many-body structure calculations typically use a bound state basis, while reaction calculations more commonly utilize few-body continuum approaches. Here, the many-body bound state and few-body continuum methods use different language and emphasize different properties. To build better foundations for these bridges, we present an overview of several bound state and continuum methods and, where possible, point to current and possible future connections.
Abstract Baryon number conservation is not guaranteed by any fundamental symmetry within the standard model, and therefore has been a subject of experimental and theoretical scrutiny for decades. So far, no evidence for baryon number violation has been observed. Large underground detectors have long been used for both neutrino detection and searches for baryon number violating processes. The next generation of large neutrino detectors will seek to improve upon the limits set by past and current experiments and will cover a range of lifetimes predicted by several Grand Unified Theories. In this White Paper, we summarize theoretical motivations and experimental aspects of searches for baryon number violation in neutrino experiments.
This white paper provides a comprehensive review of our present understanding of experimental neutrino anomalies that remain unresolved, charting the progress achieved over the last decade at the experimental and phenomenological level, and sets the stage for future programmatic prospects in addressing those anomalies. It is purposed to serve as a guiding and motivational "encyclopedic" reference, with emphasis on needs and options for future exploration that may lead to the ultimate resolution of the anomalies. We see the main experimental, analysis, and theory-driven thrusts that will be essential to achieving this goal being: 1) Cover all anomaly sectors -- given the unresolved nature of all four canonical anomalies, it is imperative to support all pillars of a diverse experimental portfolio, source, reactor, decay-at-rest, decay-in-flight, and other methods/sources, to provide complementary probes of and increased precision for new physics explanations; 2) Pursue diverse signatures -- it is imperative that experiments make design and analysis choices that maximize sensitivity to as broad an array of these potential new physics signatures as possible; 3) Deepen theoretical engagement -- priority in the theory community should be placed on development of standard and beyond standard models relevant to all four short-baseline anomalies and the development of tools for efficient tests of these models with existing and future experimental datasets; 4) Openly share data -- Fluid communication between the experimental and theory communities will be required, which implies that both experimental data releases and theoretical calculations should be publicly available; and 5) Apply robust analysis techniques -- Appropriate statistical treatment is crucial to assess the compatibility of data sets within the context of any given model.
The determination of the dielectric breakdown strengths of microstructurally heterogeneous materials has been a primarily experimental endeavor. We report the development of a microstructure-level model for computationally predicting the breakdown strength and analyzing the interactions between electromagnetic pulses (EMP) and the constituents in a composite of cellulose-based paper and mineral oil found in electrical transformers. The model allows explicit simulation of the material breakdown process by tracking the transition of dielectric constituents from non-conductive to conductive states. The focus is on the electric fields induced in the materials and the overall conditions for dielectric breakdown (defined as the onset of avalanche) caused by the electric field induced in the composite. Responses to three distinct pulse shapes, i.e., Steep Front (SF), Lightning (L), and AC with spectra spanning 60–9 × 105 Hz are considered. It is found that the breakdown strength of the material is significantly affected by microstructure heterogeneities, the spatial variations of the constituent properties, and the pulse shapes. A probabilistic characterization of the breakdown strength is computationally obtained and compared with experimental measurements. Although one particular material is analyzed, the model and approach are applicable to other heterogeneous materials as well.
Molecular similarity search has been widely used in drug discovery to rapidly identify structurally similar compounds from large molecular databases. With the increasing size of chemical libraries, there is growing interest in the efficient ac- celeration of large-scale similarity search. Existing works mainly focus on CPU and GPU to accelerate the computation of Tatimoto coefficient in measuring the pairwise similarity between different molecular fingerprints. In this paper, we propose and optimize an FPGA-based accelerator design on exhaustive and approximate search algorithms. On exhaustive search using BitBound & fold- ing, we analyze the similarity cutoff and folding level relationship with search speedup and accuracy, and propose a scalable on- the-fly query engine on FPGAs to reduce the resource utilization and pipeline interval. We achieve a 450 million compounds-per- second processing throughput for a single query engine. On approximate search using hierarchical navigable small world (HNSW), a popular algorithm with high recall and query speed, we propose an FPGA-based graph traversal engine to utilize high throughput register array based priority queue and fine- grained distance calculation engine to increase the processing capability. Experimental results show that the proposed FPGA- based HNSW implementation achieves a 35× speedup than existing works on CPU. To the best of our knowledge, our FPGA- based implementation is the first attempt to accelerate molecular similarity search on FPGA and has the highest performance among existing approaches.
Abstract Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.
Neutrinos are one of the most promising messengers for signals of new physics Beyond the Standard Model (BSM). On the theoretical side, their elusive nature, combined with their unknown mass mechanism, seems to indicate that the neutrino sector is indeed opening a window to new physics. On the experimental side, several long-standing anomalies have been reported in the past decades, providing a strong motivation to thoroughly test the standard three-neutrino oscillation paradigm. In this Snowmass21 white paper, we explore the potential of current and future neutrino experiments to explore BSM effects on neutrino flavor during the next decade.
This document describes the supplemental materials for the paper: TITLE: "Radiosonde-to-Space (R2S) atmospheric specifications: Bridging observations and models for infrasound propagation" DOI: xxxxxxxxxxx JOURNAL: TBD Written by Loring Schaible, September 17, 2025 This folder contains eight subfolders, each for a specific date and UTC time (in format YYYY-MM-DD_HH). All eight of these dates are exemplified and described in the manuscript. Within each folder is six documents. As an example, consider the folder "2022-12-31_00". The six files are comprised of: - Two text files (extension .txt) that describe the atmospheric specifications of Albuquerque, NM. One is the G2S model, the other is the R2S model. "G2S_2022-12-31_00.txt" "R2S_2022-12-31_00.txt" - Two text files (extension .dat) that contain the ground arrivals predicted by infraGA using the parameters described in the manuscript. One each for the two atmospheric specification models above, G2S and R2S. "G2S_2022-12-31_00.arrivals.dat" "R2S_2022-12-31_00.arrivals.dat" - Two image files (extension .png) that illustrate the predicted arrivals contained in the two files above. One image shows all arrivals as either black (G2S) or red (R2S). The other image shows the same but with arrivals common to both models in gray. "2022-12-31_00_Arrivals.png" "2022-12-31_00_DifArrivals.png"
Artifact Description of the Cerebras TriLabs SC'24 conference paper. Include details and procedures for how to reproduce timing computations.