Data Integration for a Microreactor Digital Twin
A poster detailing the data integration of a microreactor digital twin.
97 - MATHEMATICS AND COMPUTING↗
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Results for “Twinning”
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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NNSA Digital Twin to Detect Nuclear Proliferation (SAC Rapidfire)
This is a short presentation on the NNSA digital twin for proliferation detection, for a scientific advisory committee meeting
artificial intelligence↗
Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors
Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Digital Twins for Nuclear Power Plants and Facilities
The nuclear digital twin (DT) is the virtual representation of a nuclear energy system across its lifecycle. The nuclear DT uses real-time information and other data sources to improve the process of design, licensing, construction, security, O&M, decommissioning, and waste disposal. By leveraging the knowledge base and experience from the past 40 years of LWR operation, the nuclear DT is helping to accelerate the development and deployment of advanced nuclear technology in areas of passive safety, new fuel forms, instrumentation, and reactor control. For the currently operating nuclear fleet, DTs are reducing the operational risks, increasing plant availability, increasing energy capability, and reducing electricity production costs. For advanced fission and fusion reactors, DTs are being used to design for passive safety and built-in security-by-design. Rapidly deployable small modular reactor (SMR) and microreactor designs compatible with modular construction techniques and advanced manufacturing will be the new normal, reducing the need for large capital expenditures and compressing construction schedules. In addition, lower operational and maintenance costs will be realized by reducing the complexity of operations, staffing needs, and maintenance-related activities.
Kropaczek, Dave↗
A meshing framework for digital twins for extrusion based additive manufacturing
Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.
Additive manufacturing↗
Transformation and twinning induced plasticity in metastable Ti-Mo alloys under high strain rate deformation
The development of metastable titanium (Ti) alloys provides an unprecedented opportunity to expand their use in plasticity and damage critical applications like protective structures. However, such applications require knowledge of quasi-static to dynamic mechanical behavior, which is currently lacking. Here we perform in-situ, ultrafast synchrotron x-ray diffraction during high strain rate (Kolsky) pressure bar testing in tension and post-mortem electron microscopy to study TRansformation Induced Plasticity (TRIP) and TWinning Induced Plasticity (TWIP) in metastable Ti-Mo alloys. Two alloys, namely Ti-12Mo and Ti-15Mo (wt.%), were selected for study having different β -phase chemical stabilities. TWIP was observed in both Ti-12Mo and Ti-15Mo by in-situ synchrotron diffraction during high strain rate testing. Post-mortem microstructural characterization also revealed the presence of TRIP in Ti-12Mo. TWIP in Ti-15Mo was found to under-perform in terms of total elongation compared to TRIP/TWIP in Ti-12Mo. Ti-12Mo exhibited an average elongation of 17% compared to only 12% for Ti-15Mo with deformation at 1000 s -1 . In conclusion, TRIP resulted in significantly finer microstructure evolution and alleviated local strain accumulations in Ti-12Mo, suggesting TRIP can be used to tune available strength/ductility combinations in metastable Ti alloys under high strain rate deformation conditions.
36 MATERIALS SCIENCE↗
Direct Observation of Twin van der Waals Molecular Chains
The van der Waals (vdW) assemblies are the most common structures of materials. However, directly mapping intermolecular electron clouds of a vdW assembly has never been obtained, even though the intramolecular electron clouds were visualized by atomic-resolution techniques. In this report, we unprecedently mapped the intermolecular electron cloud of the assembles of ethanol molecules via ethyl groups with noncontact atomic force microscopy at 5 K, leading to the first visualization of vdW molecular chains, in which ethanol molecules assemble into twin vdW molecular chains in a reverse parallel configuration on Ag(111) plane. Furthermore, spontaneous order-disorder transitions in the chain were dynamically observed, suggesting its unusual properties different from 2D vdW materials. In conclusion, these findings provide an "eye" to see the atomic world of vdW materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides
Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process from the parent to Janus structure. Raman spectroscopy stands out for such a task as it is a powerful, nondestructive, and very commonly used tool to characterize 2D materials both in situ and ex situ. To realize the full potential of Raman spectroscopy on rapid characterization of Janus TMDs, we present a computational “Raman digital twin” library for various monolayer Janus TMDs in both 2H and Td phases. We focus on group-6 TMDs: MoS 2 , WS 2 , MoSe 2 , WSe 2 , MoTe 2 , WTe 2 and their Janus variants: MoSSe, MoSTe, MoSeTe, WSSe, WSTe, and WSeTe. Using first-principles density functional theory (DFT), we calculate their vibrational properties and predict distinct Raman fingerprints. These phonon and Raman signatures reflect each material’s structural symmetry and atomic composition, enabling clear identification via Raman spectroscopy. Our theoretical work supports experimental efforts by providing benchmarks for material identification, structural analysis, and quality control. In conclusion, the computational library expedites the discovery and development of Janus 2D materials, facilitating tighter integration between theoretical predictions and experimental validation.
Chalcogenides↗
Monitoring Fracture Saturation With Internal Seismic Sources and Twin Neural Networks
Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply scattered or reflected waves. However, a challenge arises when multiply scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural or induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (TNN also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The TNN with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. Furthermore, these results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.
58 GEOSCIENCES↗
A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins
Abstract Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.
Mathematical & Computational Biology↗
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Abstract We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT–AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of $$7 \times 10^{6}$$ 7 × 10 6 times, in the best case, and $$7 \times 10^{3}$$ 7 × 10 3 times on average compared to a finite element solver. Furthermore, this BT–AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.
97 MATHEMATICS AND COMPUTING↗
Matryoshka phonon twinning in α-GaN
Understanding lattice dynamics is crucial for effective thermal management in electronic devices because phonons dominate thermal transport in most semiconductors. α -GaN has become a focus of interest as one of the most important third-generation power semiconductors, however, the knowledge on its phonon dynamics remains limited. Here we show a Matryoshka phonon dispersion of α -GaN with the complementary inelastic X-ray and neutron scattering techniques and the first-principles calculations. Such Matryoshka twinning throughout the basal plane of the reciprocal space is demonstrated to amplify the anharmonicity of the related phonons through creating abundant three-phonon scattering channels and cutting the lifetime of affected modes by more than 50%. Such phonon topology contributes to reducing the in-plane thermal transport, thus the anisotropic thermal conductivity of α -GaN. The results not only have implications for engineering the thermal performance of α -GaN, but also offer valuable insights on the role of anomalous phonon topology in thermal transport of other technically semiconductors.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface
Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag–H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner.
Chemistry↗
ZTF-SEDm Type Ia supernova sample for Twins Embedding spectrophotometric standardization
Aims. This paper has two aims: the first aim is to build a large homogeneous spectrophotometric sample of Type Ia supernovae (SNe Ia) from the second data release of the Zwicky Transient Facility (ZTF DR2). We used the spectrum sample from the low-resolution ( R ∼ 100) SEDmachine (SEDm) Integral Field Spectrograph (IFS) that gathered 3069 spectra. This is one of the largest samples of such collections that can attempt to reproduce the Twins Embedding (TE) spectrophotometric standardization method. This is our second objective. The method was developed based on high-quality spectra from 200 SNe Ia of the Nearby Supernova factory (SNfactory) and led to an exceptionally low value of 0.073 mag for the intrinsic scatter. Methods. As the SEDm is not designed as a spectrophotometric instrument, we first improved the flux-calibration accuracy of the SN Ia spectrum sample using the ZTF photometric data, which were calibrated at the percent level. We corrected the spectra for second-order polynomials, fitted by comparing the synthetic photometry in the ZTF g , r , i filters with the light-curve (LC) data. We then applied the three steps of the TE parameterization to a subset of 783 ZTF SN spectra near maximum light while comparing results from SNfactory and ZTF. We finally analyzed the standardization methods based on the TE parameters. Results. The precision of the phase-correction model, which is the first step of the TE, is estimated at 0.01 mag in g band based on ZTF data. Despite the challenge posed by the spectrum-extraction pipeline associated with the SEDm (flux calibration, leftover host signal, low signal-to-noise ratio, and low resolution), we applied a first standardization in color based on the second step of the TE, called read between the lines (RBTL), to the ZTF sample. We reached a Hubble residual scatter of 0.153 mag, all in normalized median absolute deviation, which is to be compared to the ∼0.11 mag obtained with the SNfactory data. The SALT color and stretch standardization reaches a scatter of 0.164 mag for the same ZTF SN Ia sample, and its host steps are ∼0.1 mag and zero for RBTL. When considering the scatter due to the redshift error and flux calibration error, we estimated a RBTL scatter of ∼0.129 mag for this ZTF sample as an upper limit because we identified an additional contribution from a systematic error in color. We tested the standardization based on the nonlinear TE parameters, and, as expected from the low spectrum quality, it did not improve the overall dispersion. Conclusions. We release 1897 flux calibrated spectra of 1607 SNe Ia with an estimated photometric accuracy of 0.07 mag. We further demonstrate that some amount of spectrophotometric SN Ia standardization can be done with limited-quality spectra. The RBTL standardization is more efficient than that of SALT with one parameter less, and the resulting host steps are consistent with zero. This makes it less prone to astrophysical bias. For future spectroscopic surveys, targeting the extraction pipeline for a thorough flux calibration and good signal-to-noise ratio would enable us to compute the full TE standardization, which would further reduce the scatter in the distance estimate.
Ganot, C↗
Natural supersymmetric Twin Higgs
A new class of supersymmetric Twin Higgs (TH) models where new gauge symmetry is responsible for the TH mechanism is reviewed. In this class of models the Higgs mass is naturally in agreement with the LHC measurement while the electroweak symmetry breaking is realised without excessive tuning despite of strong lower bounds on masses of supersymmetric particles set by the LHC. Assuming particular non-abelian structure of the new gauge symmetry the model remains perturbative up to the energy scale of gravity, in contrast to all previously proposed UV completions of the TH model.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Uncovering Hidden Entanglement in Twin Beams
Proper characterization of quantum correlations in multimode optical quantum states is critical for applications in quantum information science. However, standard entanglement measurements can lead to incomplete state reconstruction and characterization. Here, we implement a resonator-based detection system that reveals entanglement between sideband modes of twin beams, achieving full tomography and retrieving often ignored quantum correlations. Unlike standard spectral measurements such as homodyne detection, resonator detection can independently address the sidebands of each beam, thereby accessing these hidden correlations. Additionally, we show how phase shifts between the carrier and the sideband modes of the involved fields redistribute information and modify the observation of entanglement for different witnesses. The ability of the resonant detection to independently address sideband modes of entangled states can contribute to enhancing the capacity for secure communication and quantum networking protocols.
Rincon Celis, Raul [University of Sao Paulo, Brazi↗
Kramers nodal line in the charge density wave state of YTe 3 and the influence of twin domains
Recent studies have focused on the relationship between charge density wave (CDW) collective electronic ground states and nontrivial topological states. YTe 3 , a nonmagnetic quasi-two-dimensional chalcogenide, has been reported to exhibit a CDW state below 334 K. Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), we establish that YTe 3 is a CDW-induced Kramers nodal line (KNL) metal, a recently proposed topological state of matter. Scanning tunneling microscopy and low energy electron diffraction reveal two orthogonal domains, each with a unidirectional CDW and a similar wave vector (𝐪 CDW ). When the influence of twin domains is considered, the effective band structure (EBS) computations that utilize DFT-calculated bands using a noncentrosymmetric structure determined by x-ray crystallography, show excellent agreement with ARPES. The noncentrosymmetry of YTe 3 is established by Raman spectroscopy. The Fermi surface and ARPES intensity plots show weak shadow bands displaced by 𝐪 CDW from the main bands. Furthermore, these are linked to CDW modulation, as the EBS calculation confirms. Bilayer split main and shadow bands suggest the existence of crossings, according to theory and experiment. DFT bands, including spin-orbit coupling, indicate existence of a KNL along the Σ direction from multiple crossings of bands dispersing perpendicular to it. Additionally, doubly degenerate bands are only found along the KNL at all energies, with some bands dispersing through the Fermi level.
Angle-resolved photoemission spectroscopy↗
Digital Twin-Enabled Adaptive Control for Hydroelectric Systems: Turbine Governor and Voltage Regulation
This paper presents a comprehensive digital twin (DT) framework for hydroelectric systems that enables adaptive control of turbine governors and excitation systems without requiring detailed manufacturer specifications. The proposed framework integrates neural network-based system identification with stabilizing adaptive control laws for the installed turbine controller and middle-branch adaptive tuning for the installed voltage regulator. Using real operational data from Unit C-8 at Rocky Reach Dam (1,349 MW capacity), highfidelity neural network models are developed to capture turbine and generator dynamics without requiring detailed manufacturer specifications. The DT enables safe controller synthesis and validation in simulation before deployment. For turbine control, the proposed method achieves a 79.9% mean square error (MSE) reduction compared with that of an optimal controller. For voltage regulation, the adaptive excitation controller achieves approximately 42.6% MSE reduction while preserving installed protection logic. The results demonstrate that DT technology provides a practical pathway for modernizing hydropower control systems with minimal operational disruption.
Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗