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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of Micro-Structural Dispersity of SiMo Ductile Iron on High Temperature Performance during Static Oxidation

High silicon and molybdenum (SiMo) ductile iron is commonly used for car exhaust systems, and its micro-structural dispersity depends on intrinsic parameters, which include alloy composition and inoculation efficiency, as well as extrinsic factors, such as casting wall thickness and molding material, which define cooling rate during solidification. Micro-structural dispersity is referred to as the degree of heterogeneity of sizes of structural constituencies within the microstructure. A variation in the micro-structural dispersity could impact the high temperature performance of SiMo ductile iron during static oxidation and transient thermo-mechanical loading conditions. In this study, static high temperature tests were performed on SiMo ductile iron solidified in a casting with varying wall thicknesses from 5 mm to 100 mm. The faster solidified specimens (taken from near chilled casting surfaces) had extremely high micro-structural dispersity as compared to the thicker section samples. After thermal exposure, each of the samples were characterized using 2D sections and 3D µCT images, and the results indicated an order of magnitude difference in graphite phase dispersity. The surface degradation was quantified after static oxidation experiments were implemented at temperature intervals between 650 °C and 800 °C. Non-destructive µCT 3D analysis and SEM/EDS were performed on cross sections and used to quantify the scale topology and structure. Carbon analysis was used to decouple the scale formation and decarburization phenomena that occurred within the samples. These methods enabled the quantification of the oxidation of the SiMo cast iron with different micro-structural dispersity levels after being exposed to high temperature static oxidation. Additionally, the complex material behavior during oxidation-assisted transient thermo-mechanical loading will be presented in a separate article.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electromagnetic Design Optimization Integrated with Mechanical Stress Analysis of PM-Assisted Synchronous Reluctance Machine Topologies Enabled with a Blend of Magnets

Permanent Magnet-Assisted Synchronous Reluctance Machines (PMASynRM) provide a low-cost alternative to Surface PM Machines due to the use of relatively lower grades of rare-earth (RE) or RE-free magnets, as the performance degradation due to weaker magnets is compensated by the presence of reluctance torque. However, the weaker magnets suffer from a high risk of demagnetization, leading to unreliable motor operation. Using a blend of RE and RE-free magnets has the potential to overcome this issue. This paper proposes to blend different grades of various rare-earth (RE) and rare-earth-free (RE-free) magnets in six different combinations and utilizes them in two-layer and three-layer U-shaped PMASynRM topologies with both eight-pole and six-pole variations. The rotor of the various designs is then optimized using a differential evolution (DE) based optimization algorithm to obtain low-cost designs with reduced RE magnet volume and minimum demagnetization risk. The optimization of each design is also integrated with the evaluation of mechanical stresses in the rotor laminations so as to maintain the stresses below the material yield strength. Furthermore, the various performance metrics, such as toque–speed/power–speed characteristics, demagnetization, and efficiency maps, are evaluated for each of the optimized and mechanically feasible designs. A quantitative comparison of the various optimized designs is also obtained to highlight the various trade-offs. The results indicate the feasibility of meeting the baseline torque requirement across the entire speed range, even with a 100% reduction in RE magnet volume and less than 5% demagnetization risk, while achieving a cost reduction exceeding 50%. Moreover, the two-layer, eight-pole designs exhibit relatively higher performance, whereas the three-layer, eight-pole designs are found to be the most economical option.

design optimization↗
DOE OSTI2024

Fermiology and transport properties of the candidate topological crystalline insulator SrAg 4 ⁢Sb 2

Compared to time-reversal symmetry-protected ℤ 2 topological insulators and Dirac/Weyl semimetals, there are significantly fewer candidates for topological crystalline insulators. SrAg 4 ⁢Sb 2 is predicted to exhibit topological crystalline insulator behavior when considering spin-orbit coupling. In this study, we systematically investigate single crystals of SrAg 4 ⁢Sb 2 using electrical transport and magnetic torque measurements, along with first-principles calculations. Our transport data reveals its compensated semimetal nature with a magnetoresistance up to around 700% at 2 K and 9 T. Analysis of de Haas–van Alphen oscillations uncovers a Fermi surface consisting of three distinct Fermi pockets with light effective masses. Comparison between the three-dimensional fermiology obtained from our oscillation data and the first-principles calculations demonstrates excellent agreement. This confirms the accuracy of the calculations, which indicate a band inversion centered at the 𝑇 point and identify the existence of nontrivial tube and needle hole Fermi pockets at Γ, alongside one trivial diamond electron pocket at the 𝐹 point in the Brillouin zone. Furthermore, symmetry and topology analysis results in two potential sets of topological invariants, suggesting the emergence of two-dimensional gapless Dirac surface states either on the 𝑎⁢𝑏 planes or on both the 𝑎⁢𝑏 planes and mirror planes, protected by crystal symmetries. Therefore, SrAg 4 ⁢Sb 2 emerges as a promising candidate topological crystalline insulator.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Topological Equivalence of VSI and CSI Commutation Cells and Its Application to Switching Resonance Analysis and Damper Design

This paper proposes the concept of the topological equivalence between the commutation cells of a voltage source inverter (VSI) and a current source inverter (CSI) using duality and equivalent circuit transformation techniques. A switching resonance of CSI is analyzed and an effective three-phase RC damper is derived from the dc-link RC damper of VSIs based on the topological equivalence of the VSI and CSI commutation cells. Furthermore, a new three-phase RC damper design framework utilizing the topological equivalence is proposed. Here, experimental results verify that the proposed three-phase RC damper design for CSI significantly reduces the switching resonance and improves the CSI’s output waveform total harmonics distortions (THDs).

42 ENGINEERING↗
DOE OSTI2021

Giant topological Hall effect in centrosymmetric tetragonal Mn 2–x Zn x Sb

Topological magnetism typically appears in noncentrosymmetric compounds or compounds with geometric frustration. Here, we report the effective tuning of magnetism in centrosymmetric tetragonal Mn 2–x Zn x Sb by Zn substitution. The magnetism is found to be closely coupled to the transport properties, giving rise to a very large topological Hall effect with fine-tuning of Zn content, which even persists to high temperature (~250K). The further magnetoentropic analysis suggests that the topological Hall effect is possibly associated with topological magnetism. Furthermore, our finding suggests Mn 2–x Zn x S is a candidate material for a centrosymmetric tetragonal topological magnetic system, offering opportunities for studying and tuning spin textures and developing near room temperature spin-based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Joint Search for Muon Neutrino Disappearance with the Short-Baseline Neutrino Program Using the SPINE Deep Learning-Based Reconstruction Package

We present the status of a joint search for muon neutrino disappearance in the Booster Neutrino Beam at Fermilab using the Short-Baseline Neutrino (SBN) Program's two-detector configuration, SBND and ICARUS. Charged-current interactions consistent with muon neutrinos and containing only a muon and at least one proton in the final state are reconstructed and selected using the SPINE deep learning-based particle reconstruction package. To exploit proton multiplicity information and enhance sensitivity to modeling effects, the selected sample is partitioned into exclusive channels with exactly one reconstructed proton and with more than one reconstructed proton. Comprehensive systematic uncertainties from the neutrino flux, interaction, and detector response are incorporated into the analysis, and the coverage of these systematic uncertainty models is validated using data from both detectors, including checks of near-far consistency in key kinematic and topology-sensitive observables. This analysis is intended for inclusion in SBN's first oscillation result.

Mueller, Justin [Fermilab]↗
DOE OSTI2020

Quantum field theory of topological spin dynamics

Here, we develop a field theory of quantum magnets and magnetic (semi)metals, which is suitable for the analysis of their universal and topological properties. The systems of interest include collinear, coplanar, and general noncoplanar magnets. At the basic level, we describe the dynamics of magnetic moments using smooth vector fields in the continuum limit. Dzyaloshinskii-Moriya interaction is captured by a non-Abelian vector gauge field, and chiral spin couplings related to topological defects appear as higher-rank antisymmetric tensor gauge fields. We distinguish type-I and type-II magnets by their equilibrium response to the non-Abelian gauge flux, and characterize the resulting lattices of skyrmions and hedgehogs, the spectra of spin waves, and the chiral response to external perturbations. The general spin-orbit coupling of electrons is similarly described by non-Abelian gauge fields, including higher-rank tensors related to the electronic Berry flux. Itinerant electrons and local moments exchange their gauge fluxes through Kondo and Hund interactions. Hence, by utilizing gauge fields, this theory provides a unifying physical picture of “intrinsic” and “topological” anomalous Hall effects, spin-Hall effects, and other correlations between the topological properties of electrons and moments. We predict “topological” magnetoelectric effect in materials prone to hosting hedgehogs. Links to experiments and model calculations are provided by deriving the couplings and gauge fields from generic microscopic models, including the Hubbard model with spin-orbit interactions. Much of the formal analysis is generalized to d spatial dimensions in order to access the $π_{d–1}(S^{d–1})$ homotopy classification of the magnetic hedgehog topological defects, and establish the possibility of novel quantum spin liquids that exhibit a fractional magnetoelectric effect. However, we emphasize the form of all results in the physically relevant $\textit{d}$ = 3 dimensions, and discuss a few applications to topological magnetic conductors like Mn 3 Sn and Pr 2 Ir 2 O 7 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2024

Topological and magnetic properties of the interacting Bernevig-Hughes-Zhang model

We investigate the effects of electronic correlations on the Bernevig-Hughes-Zhang model using the real-space density matrix renormalization group (DMRG) algorithm. We introduce a method to probe topological phase transitions in systems with strong correlations using DMRG, substantiated by an unsupervised machine learning methodology that analyzes the orbital structure of the real-space edges. Including the full multi-orbital Hubbard interaction term, we construct a phase diagram as a function of a gap parameter (m) and the Hubbard interaction strength (U) via exact DMRG simulations on N×4 cylinders. Our analysis confirms that the topological phase persists in the presence of interactions, consistent with previous studies, but it also reveals an intriguing phase transition from a paramagnetic to a stripey antiferromagnetic topological insulator. The combination of the magnetic structure factor, strength of magnetic moments, and the orbitally resolved density, provides real-space information on both topology and magnetism in a strongly correlated system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Controlling topology through targeted composite symmetry manipulation in magnetic systems

The possibility of selecting magnetic space groups by orienting the magnetization direction or tuning magnetic orders offers a vast playground for engineering symmetry-protected topological phases in magnetic materials. In this work, we study how selective tuning of symmetry and magnetism can influence and control the resulting topology in a two-dimensional magnetic system, and we illustrate such a procedure in the ferromagnetic monolayer MnPSe 3 . Density functional theory calculations reveal a symmetry-protected accidental semimetallic (SM) phase for out-of-plane magnetization, which becomes an insulator when the magnetization is tilted in-plane, reaching band-gap values close to 100 meV. We identify an order-2 composite antiunitary symmetry and threefold rotational symmetry that induce the band crossing, and we classify the possible topological phases using symmetry analysis, which we support with tight-binding and k · p models. Breaking of inversion symmetry opens a gap in the SM phase, giving rise to a Chern insulator. We demonstrate this explicitly in the isostructural Janus compound Mn 2 ⁢P 2 ⁢S 3 ⁢Se 3 , which naturally exhibits Rashba spin-orbit coupling that breaks inversion symmetry. Our results map out the phase space of topological properties of ferromagnetic transition-metal phosphorus trichalcogenides, and they demonstrate the potential of the magnetization-dependent metal-to-insulator transition as a spin switch in integrated two-dimensional electronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗
DOE OSTI2022

Local bilinear computation of Jacobi sets

Abstract We propose a novel method for the computation of Jacobi sets in 2D domains. The Jacobi set is a topological descriptor based on Morse theory that captures gradient alignments among multiple scalar fields, which is useful for multi-field visualization. Previous Jacobi set computations use piecewise linear approximations on triangulations that result in discretization artifacts like zig-zag patterns. In this paper, we utilize a local bilinear method to obtain a more precise approximation of Jacobi sets by preserving the topology and improving the geometry. Consequently, zig-zag patterns on edges are avoided, resulting in a smoother Jacobi set representation. Our experiments show a better convergence with increasing resolution compared to the piecewise linear method. We utilize this advantage with an efficient local subdivision scheme. Finally, our approach is evaluated qualitatively and quantitatively in comparison with previous methods for different mesh resolutions and across a number of synthetic and real-world examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Fast Algorithm for Computing Zigzag Representatives

Zigzag filtrations of simplicial complexes generalize the usual filtrations by allowing simplex deletions in addition to simplex insertions. The barcodes computed from zigzag filtrations encode the evolution of homological features. Although one can locate a particular feature at any index in the filtration using existing algorithms, the resulting representatives may not be compatible with the zigzag: a representative cycle at one index may not map into a representative cycle at its neighbor. For this, one needs to compute compatible representative cycles along each bar in the barcode. It is known that the barcode for a zigzag filtration with m insertions and deletions can be computed $O(m^ω)$ in time, where $ω < 2.373$ is the matrix multiplication exponent. However, it is not known how to compute the compatible representatives so efficiently. For a non-zigzag filtration, the classical matrix-based algorithm provides representatives in $O(m^3)$ time, which can be improved to $O(m^ω)$. However, no known algorithm for zigzag filtrations computes the representatives with the $O(m^3)$ time bound. We present an $O(m^3 n)$ time algorithm for this problem, where $n ≤ m$ is the size of the largest complex in the filtration.

Persistent homology↗
DOE OSTI2022

Tuning Metal–Organic Framework (MOF) Topology by Regulating Ligand and Secondary Building Unit (SBU) Geometry: Structures Built on 8-Connected M 6 (M = Zr, Y) Clusters and a Flexible Tetracarboxylate for Propane-Selective Propane/Propylene Separation

Topology evolution originating from variations of linker and SBU (Secondary Building Unit) geometries could largely enrich the chemistry of metal–organic frameworks (MOFs). Here we report the synthesis and characterization of three MOF structures built on the same organic linker, N,N,N',N'-Tetrakis(4-carboxyphenyl)-1,4-phenylenediamine (tcppda) and similar 8-connected M 6 (M = Zr or Y) clusters. The three compounds, HIAM-402, HIAM-403, and HIAM-311, feature 4,8-connected sqc, scu, and flu topology, respectively. Detailed structural analysis revealed that different geometries of the inorganic M 6 SBUs as well as the organic linker have led to the formation of distinct MOF nets. In particular, HIAM-402 features exceptional framework stability and high porosity and acts as a propane-selective adsorbent for the discrimination of propane and propylene. Its balanced adsorption selectivity (S propane/propylene = 1.43) and capacity (Q propane = 133.3 cm 3 /g, 298 K and 1 bar) endow it with the capability of separating propane and propylene mixtures and one-step production of highly pure propylene (purity >99.9%), as validated by column breakthrough measurements, with the presence of moisture or propyne. As a result, ab initio calculations further confirm that the propane-selective behavior of HIAM-402 is a result of its higher binding energy toward propane compared to that of propylene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Search for 𝛾⁢𝐻 production and constraints on the Yukawa couplings of light quarks to the Higgs boson

A search for 𝛾⁢𝐻 production is performed with data from the CMS experiment at the LHC corresponding to an integrated luminosity of 138 fb −1 at a proton-proton center-of-mass collision energy of 13 TeV. The analysis focuses on the topology of a boosted Higgs boson recoiling against a high-energy photon. The final states of 𝐻 → $b\bar{b}$ and 𝐻 → 4⁢ℓ are analyzed. This study examines effective 𝐻⁡𝑍⁢𝛾 and 𝐻⁡𝛾⁢𝛾 anomalous couplings within the context of an effective field theory. In this approach, the production cross section is constrained to be 𝜎 𝛾⁢𝐻 < 16.4 fb at 95% confidence level (CL). Simultaneous constraints on four anomalous couplings involving 𝐻⁡𝑍⁢𝛾 and 𝐻⁡𝛾⁢𝛾 are provided. Additionally, the production rate for 𝐻 → 4⁢ℓ is examined to assess potential enhancements in the Yukawa couplings between light quarks and the Higgs boson. Assuming the standard model values for the Yukawa couplings of the bottom and top quarks, the following simultaneous constraints are obtained: 𝜅 𝑢 =(0.0 ± 1.5) ×10 3 , 𝜅 𝑑 = (0.0$^{+6.7}_{−6.8}$) × 10 2 , 𝜅 𝑠 = 0$^{+30}_{−32}$, and 𝜅 𝑐 =0.0$^{+2.3}_{−2.8}$. This rules out the hypothesis that up- or down-type quarks in the first or second generation have the same Yukawa couplings as those in the third generation, with a CL greater than 95%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Localized Evaluation for Constructing Discrete Vector Fields

Topological abstractions offer a method to summarize the behavior of vector fields, but computing them robustly can be challenging due to numerical precision issues. One alternative is to represent the vector field using a discrete approach, which constructs a collection of pairs of simplices in the input mesh that satisfies criteria introduced by Forman's discrete Morse theory. While numerous approaches exist to compute pairs in the restricted case of the gradient of a scalar field, state-of-the-art algorithms for the general case of vector fields require expensive optimization procedures. This paper introduces a fast, novel approach for pairing simplices of two-dimensional, triangulated vector fields that do not vary in time. The key insight of our approach is that we can employ a local evaluation, inspired by the approach used to construct a discrete gradient field, where every simplex in a mesh is considered by no more than one of its vertices. Specifically, we observe that for any edge in the input mesh, we can uniquely assign an outward direction of flow. We can further expand this consistent notion of outward flow at each vertex, which corresponds to the concept of a downhill flow in the case of scalar fields. Working with outward flow enables a linear-time algorithm that processes the (outward) neighborhoods of each vertex one-by-one, similar to the approach used for scalar fields. Here, we couple our approach to constructing discrete vector fields with a method to extract, simplify, and visualize topological features. Empirical results on analytic and simulation data demonstrate drastic improvements in running time, produce features similar to the current state-of-the-art, and show the application of simplification to large, complex flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗