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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cyanobacteriochromes active in the far-red to near-infrared

The present invention provides a protein fusion construct comprising a far-red cyanobacteriochrome (CBCR) domain linked to a heterologous domain, wherein the far-red CBCR domain comprises a CBCR polypeptide and a tetrapyrrole chromophore. The invention also provides nucleic acids, expression cassettes, vectors, and host cells for expression of the far-red CBCR protein fusion constructs. Methods for detecting cellular components, methods for imaging biological structures, and method for modulating cellular processes using the protein fusion constructs are also provided.

Lagarias, John Clark↗
DOE OSTI2023

Multiscale Imaging of Nuclear Deformation at the Electron-Ion Collider

We show within the Color Glass Condensate framework that exclusive vector meson production at high energy is sensitive to the geometric deformation of the target nucleus at multiple length scales. Studying e+U collisions and varying the deformation of the uranium target, we demonstrate that larger deformations result in enhanced incoherent vector meson production cross sections. Further, different multipole deformation parameters affect different regions of transverse momentum transfer. Employing JIMWLK evolution to study the Bjorken-x dependence of our results, we find that the ratio of incoherent to coherent cross sections decreases with decreasing x, largely independently of the quadrupole deformation of the target. Comparing results for the same process using 20 Ne targets with 16 O targets, we find that differences in deformation are clearly visible in the incoherent cross section. These findings show that certain observables at the Electron-Ion Collider are very sensitive to nuclear structure. Consequently, deformations need to be taken into account when interpreting experimental results. More importantly, this also means that |t|-differential diffractive vector meson production could become a powerful tool, enabling the most direct measurements of nuclear structure at different length scales, ranging from nuclear deformation at low |t| to nucleon and subnucleon-size scales at higher |t|.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.

42 ENGINEERING↗
DOE OSTI2024

Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the CMS experiment

The LHC has provided an unprecedented amount of proton-proton collision data, bringing forth exciting opportunities to address fundamental open questions in particle physics. These questions can potentially be answered by performing searches for very rare processes predicted by models that attempt to extend the standard model of particle physics. The data collected by the CMS experiment in 2015-2018 at a center-of-mass energy of 13 TeV can be used to test the standard model with high precision and potentially uncover evidence for new particles or interactions. An interesting possibility is the existence of new fermions with masses ranging from the MeV to the TeV scale. Such new particles appear in many possible extensions of the standard model and are well motivated theoretically. New fermions may explain the appearance of three generations of leptons and quarks, the mass hierarchy across the generations, and the nonzero neutrino masses. In this report, the results of searches targeting vector-like quarks, vector-like leptons, and heavy neutral leptons at the CMS experiment are summarized. The complementarity of current searches for each type of new fermion is discussed, and combinations of several searches for vector-like quarks are presented. The discovery potential for some of these searches at the High-Luminosity LHC is also discussed.

FOS: Physical sciences↗
DOE OSTI2021

Hidden Killing fields, geometric symmetries and black hole mergers

Highlights: • Construction of geometric models with a variable degree of symmetry. • Formulation of Energy and Momentum Balance Laws. • Description of backreactions on spacetime geometries caused by dissipative effects. • Analytic description of accretion and/or merger processes in GR. • Exact model for the merger of two extremal Reissner–Nordström black holes. In the present work, using the recently introduced framework of local geometric deformations, special types of vector fields – so-called hidden Killing vector fields – are constructed, which solve the Killing equation not globally, but only locally, i.e. in local subregions of spacetime. Taking advantage of the fact that the vector fields coincide locally with Killing fields and therefore allow the consideration of integral laws that convert into exact physical conservation laws on local scales, balance laws in dynamical systems without global Killing symmetries are derived that mimic as closely as possible the conservation laws for energy and angular momentum of highly symmetric models. The utility of said balance laws is demonstrated by a concrete geometric example, namely a toy model for the binary merger of two extremal Reissner–Nordström black holes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Plasma image classification using cosine similarity constrained convolutional neural network

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Domain-specific text embedding model for accelerator physics

Accelerator physics presents unique challenges for natural language processing (NLP) due to its specialized terminology and complex concepts. A key component in overcoming these challenges is the development of robust text embedding models that transform textual data into dense vector representations, facilitating efficient information retrieval and semantic understanding. In this work, we introduce AccPhysBERT, a sentence embedding model fine-tuned specifically for accelerator physics. Our model demonstrates superior performance across a range of downstream NLP tasks, surpassing existing models in capturing the domain-specific nuances of the field. We further showcase its practical applications, including semantic paper-reviewer matching and integration into retrieval-augmented generation systems, highlighting its potential to enhance information retrieval and knowledge discovery in accelerator physics. Published by the American Physical Society 2025

Hellert, Thorsten (ORCID:0000000227970926)↗
DOE OSTI2024

Predictive Control to Further Reduce DC-Link Capacitor Current Stress for Segmented Inverter

Two three-phase interleaved inverters have been used in traction drive applications to reduce the current stress in a DC link capacitor bank. In such applications, either carrier-based or space vector modulation is used to select the optimum switching sequences, and the results show 50% less capacitor current than that of a single three-phase inverter. The switching state selection process for this inverter is tedious, and there has been no research to find the optimal switching state. To overcome this challenge, this research employed a simple finite set model predictive control to select the optimum switching sequence for a dual three-phase interleaved topology, called a segmented inverter. The results show that the predictive control algorithm can provide a simple solution and can reduce the current stress by 27% compared with traditional modulation techniques for the segmented inverters.

Ribeiro, Pedro↗
DOE OSTI2018

A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications

Anomalous runtime behavior detection is one of the most important tasks for performance diagnosis in High Performance Computing (HPC). Most of the existing methods find anomalous executions based on the properties of individual functions, such as execution time. However, it is insufficient to identify abnormal behavior without taking into account the context of the executions, such as the invocations of children functions and the communications with other HPC nodes. We improve upon the existing anomaly detection approaches by utilizing the call stack structures of the executions, which record rich temporal and contextual information. With our call stack tree (CSTree) representation of the executions, we formulate the anomaly detection problem as finding anomalous tree structures in a call stack forest. The CSTrees are converted to vector representations using our proposed stack2vec embedding. Structural and temporal visualizations of CSTrees are provided to support users in the identification and verification of the anomalies during an active anomaly detection process. Furthermore, three case studies of real-world HPC applications demonstrate the capabilities of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sustainable production of plastic degrading enzymes

The 21st century faces the challenge of transitioning to a climate-neutral economy. This hurdle will need to be addressed in a myriad of approaches, including all societal spheres, as highlighted in several UN and EU reports, including new and accessed technologies. Among them, biotechnology can play a core role by exploiting its environmental benefits (e.g., substituting fossil-based products and expected greenhouse emission reductions). In some cases, biotech can even outperform fossil counterparts' features (e.g., the biodegradability of some bioplastics). Microalga is an attractive bioplastic feedstock in this scenario since it directly substitutes petroleum and captures CO2. However, albeit its environmental benefits, the microalgal process still needs to be tipped economically and the net-positive ecological impact improved to drive adoption. To that end, we envisioned the use of a green alga as a source of bioplastic feedstock and industrial enzymes, combining natural traits of microalgae with engineered properties in a resourceful process to petroleum-based products replacement. As proof of concept, we demonstrated that we could use C. reinhardtii, a green algae, to produce plastic-degrading enzymes. We assemble a vector to secrete PHL7, a recently described PETase, capable of degrading post-consumer PET plastic. We successfully engineered the cell line to secrete the plastic-degrading enzyme. We confirmed the secretion of the enzyme by observing halos in agar plates supplemented with plastic dispersions, enzymatic assays, zymograms, and protein sequencing of supernatant samples. This approach allows using green algae to replace petrochemical-derived plastic while producing the tool necessary to recycle existing plastics sustainably.

Dutra Molino, Joao Vitor↗
DOE OSTI2025

Control Strategies and Validation in the Hybrid Optimization and Performance Platform (HOPP)

The Hybrid Optimization and Performance Platform (HOPP) is a tool that simulates hybrid power plants in various configurations, and also calculates the financial feasibility of these plants. This report outlines an overview of HOPP and the energy storage dispatch strategies available. It then presents three case studies which demonstrate different applications of HOPP. The first case looks at the profitability of hybrid power plants in different locations in the USA. The second case examines the availability of hybrid power plants to provide energy reliability services. The third case presents a plant that produces both hydrogen and electricity, and demonstrates a dispatch strategy that chooses the most profitable energy vector based on price signals. The next section shows the validation of HOPP on operational data, using data from both unit-scale and utility-scale power plants. This validation process demonstrated that HOPP can simulate the power output of both wind and solar PV plants at both scales with comparable fidelity to an existing commercial software tool. Finally, HOPP is applied in a field test which applies an optimal dispatch strategy to a physical battery in a unit-scale hybrid plant at NREL. HOPP's optimal dispatch strategy, applied in a real-world setting, improved this hybrid plant's ability to meet a load signal while minimizing operational costs.

14 SOLAR ENERGY↗
DOE OSTI2021

Electronic Structure Correspondence of Singlet-Triplet Scale Separation in Strained Sr2RuO4

At a temperature of roughly 1 K, Sr 2 RuO 4 undergoes a transition from a normal Fermi liquid to a superconducting phase. Even while the former is relatively simple and well understood, the superconducting state has not even been understood after 25 years of study. More recently, it has been found that critical temperatures can be enhanced by the application of uniaxial strain, up to a critical strain, after which it falls off. In this work, we take an “instability” approach and seek divergences in susceptibilities. This provides an unbiased way to distinguish tendencies to competing ground states. We show that in the unstrained compound, the singlet and triplet instabilities of the normal Fermi liquid phase are closely spaced. Under uniaxial strain, electrons residing on all orbitals contributing to the Fermiology become more coherent, while the electrons of the Ru-dxy character become heavier, and the electrons of the Ru d x z , y z characters become lighter. In the process, Im χ(q,ω) increases rapidly around q = ( 0.3 , 0.3 , 0 ) 2 π / a and q = ( 0.5 , 0.25 , 0 ) 2 π / a , while it gets suppressed at all other commensurate vectors, in particular at q = 0, which is essential for spin-triplet superconductivity. We observe that the magnetic anisotropy under strain drops smoothly, which is concomitant with the increment in singlet instability. Thus, the triplet superconducting instability remains the lagging instability of the system, and the singlet instability enhances under strain, leading to a large energy-scale separation between these competing instabilities. However, since this happens even without spin-orbit coupling, we believe it is primarily the enhancement in the spin fluctuation glue around quasi-anti-ferromagnetic vectors that drives the Cooper pairing instead of the magnetic anisotropy. At large strain, an instability to a spin density wave overtakes the superconducting one. The analysis relies on a high-fidelity, ab initio description of the one-particle properties and two-particle susceptibilities, based on the quasiparticle self-consistent GW approximation augmented by dynamical mean field theory. This approach is described and its high fidelity confirmed by comparing to observed one- and two-particle properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Photons from dark photon solitons via parametric resonance

Wave-like dark matter made of spin-1 particles (dark photons) is expected to form ground state clumps called "vector solitons", which can have different polarizations. Here, in this work, we consider the interaction of dark photons with photons, expressed as dimension-6 operators, and study the electromagnetic radiation that arises from an isolated vector soliton due to parametric resonant amplification of the ambient electromagnetic field. We characterize the directional dependence and polarization of the outgoing radiation, which depends on the operator as well as the polarization state of the underlying vector soliton. We discuss the implications of this radiation for the stability of solitons and as a possible channel for detecting mergers of vector solitons through astrophysical observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Short fish-origin DNA elements served as flanking sequences in a knockdown cloning vector enabling the generation of a functional siRNA molecule in mammalian cells and fish embryos

Improving the quality of a siRNA-knockdown cloning vector requires simpler, shorter, and more effective flanking sequences. In this study, we designed such flanking sequences based on those found in zebrafish pre-miR3906, namely, internal element (IE) 1 and IE2. We engineered a vegf-shRNA fragment flanked by an 80-bp IE1/IE2 and then inserted into the 3′ UTR of GFP reporter cDNA driven by a cytomegalovirus promoter to obtain a plasmid containing gfp-IE-vegf-shRNA-polA. Upon microinjection of this plasmid into zebrafish embryos, we found that IE flanking sequences could effectively induce the production of vegf-shRNA fragment, which was then processed into a functional siRNA to silence the target vegf121 gene. Northern blot showed that the vegf-shRNA fragment was cleaved from gfp-IE-vegf-shRNA-polA, resulting in the loss of polyA tails, subsequently degrading the remaining RNA-containing GFP. Moreover, Western blot revealed that addition of IE-based vegf-shRNA fragment could markedly decrease the expression of VEGF. Finally, to facilitate a more versatile application of the IE-based knockdown vector, we generated an inducible expression vector in which IE-vegf-shRNA was constructed downstream in a Tet-on system to generate a Tet-on-IE-vegf-shRNA construct. After doxycycline induction, the protein level of VEGF in SW620 cells harboring the Tet-on-IE-vegf-shRNA construct was decreased 77%. Interestingly, when SW620 cells harboring Tet-on-IE-vegf-shRNA cells were induced and transplanted into zebrafish embryos, we found that abnormal branch of the sub-intestinal vessels was reduced in the recipient embryos, suggesting that vegf-shRNA cleaved from Tet-on-IE-vegf-shRNA-polA was processed into a functional vegf-siRNA in embryos suppressing endogenous VEGF and reducing tumor angiogenesis. Therefore, we conclude that fish-origin IEs are flanking sequences with short, simple, and effective DNA elements. This IE-based knockdown cloning vector provides a new alternative material to facilitate the generation of functional siRNA with which to perform loss-of-function experiments, both in vitro (mammalian cells) and in vivo (zebrafish embryos).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Attacking the IEC-61131 Logic Engine in Programmable Logic Controllers in Industrial Control Systems

In industrial control systems (ICS), programmable logic controllers (PLCs) directly monitor and control a physical process such as nuclear power plants, gas pipelines, and water treatment. They are equipped with a control logic written in IEC-61131 languages (e.g., ladder logic and structured text) that defines how a PLC should control a physical process. A PLC's control logic is a usual target of a cyberattack to sabotage a physical process. For instance, Stuxnet targets a control logic of a Siemens S7-300 PLC to damage a nuclear facility's centrifuges. The existing attacks in the literature generally focus only on injecting malicious control logic into a PLC. This paper presents a new dimension of control logic attacks that target the control logic engine (responsible for running a control logic) of a PLC. It demonstrates that a cyberattack can disable the control logic engine successfully by exploiting inherent PLC features such as program mode and starting/stopping engine. We develop two novel case studies on control logic engine attacks by employing the MITRE ATT\&CK knowledge base on the real-world PLCs used in industry settings, i.e., 1) Schweitzer Engineering Laboratory (SEL)'s Real-Time Automation Controller (SEL-3505 RTAC) equipped with security features such as encrypted traffic and device-level access control, and 2) traditional PLCs, i.e., Schneider Electric's Modicon M221, Allen-Bradley's MicroLogix 1400 and 1100 that do not have security features. The case studies present the internals of the logic engine attacks and facilitate the ICS research community and industry to understand the attack vectors on the control logic engine. We evaluate the effectiveness of the control engine attacks on a power substation, a 4-floor elevator, and a conveyor belt to demonstrate their real-world impact of halting a physical process.

Ali qasim, Syed↗
DOE OSTI2023

Three‐Dimensional Broadband Interferometric Mapping and Polarization (BIMAP‐3D) Observations of Lightning Discharge Processes

Abstract Following on our earlier single‐station, 2‐dimensional (2D) broadband interferometric mapping and polarization (BIMAP) observations of lightning discharges, we recently deployed two BIMAP stations at Los Alamos National Laboratory to map the lightning sources and their polarization in full 3‐dimensional (3D) space (BIMAP‐3D). The two stations are separated by 11.5‐km and each station consists of four antenna sets (instead of three for the original BIMAP) that form a Y ‐shaped array for improved interferometric performance. In this paper, we report the BIMAP‐3D system design, a generalized and analytical 2D interferometry technique for noncoplanar antenna array, a two‐stage 3D mapping technique based on geometric triangulation and baseline‐based differential time of arrival, and a technique to reconstruct the polarization orientation in 3D space by combining the 2D polarization results from the two‐station observations. Along with description of the techniques, we demonstrate and discuss the initial lightning results, including 3D maps for a hybrid intracloud and cloud‐to‐ground flash and for a normal intracloud flash, development of abnormal K‐change leaders, and polarization signatures for a K‐change leader. We find that with the two‐stage 3D mapping techniques, the sources can be located to meters accuracy for a favorable event that occurs between the two stations. We also find the polarization vectors for the example K leader are mostly orthogonal to the leader channel after the full 3D polarization analysis. The main purpose of this paper is to report the BIMAP‐3D techniques and capabilities. Detailed analysis of more specific discharge processes will be reported in later studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Unsupervised word embeddings capture latent knowledge from materials science literature

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Lastly, our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗