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At least 955 records · Page 53

Thickness and Load-Deflection of Direct Ink Write (DIW) Polymer Parts

Parts were made by direct ink write (DIW)using a developmental 50 A durometer ink. Thickness and load-deflection were measured at four test locations on each part.Original thickness was measured using a digital thickness gauge (Checkline MTG-DX) with a 28.7-mm-diameter foot applying 0.6 kPa stress. Load-deflection was measured using an Instron 5966 equipped with a 10 kN load cell, 28.68-mm-diameter fixed lower platen, and a 50-mm-diameter spherical seat upper platen. Three load-unload cycles to a compressed thickness of 0.85 mm were performed at a test speed of 1.26 mm/min. The compressed thickness was corrected to account for instrument compliance. Original thickness is given in Table 1. Load-deflection curves(engineering stress vs. compressed thickness)are shown in Figure1. Load-deflection data(uncorrected thickness, compliance-corrected thickness, load, and engineering stress) is provided in Tables 2-4.

36 MATERIALS SCIENCE↗

Achieving Unprecedented CO 2 Utilization InCO 2 Concrete™: System Design, Product Development and Process Demonstration

Anthropogenic sources of carbon dioxide are generated from a number of sources, but the key among these are ordinary Portland cement (OPC) production and combustion of fossil fuels. Cement production is the largest global CO 2 source from the mineral decomposition of carbonates. This is due to the clinkering process whereby limestone (mainly consisting of CaCO 3 ) is decomposed into CaO and CO 2 , and combined with silica rich clays at high temperatures to form clinkers (i.e. the four key minerals that comprise cement). The high temperature range of 1400 – 1550°C required for this process accounts for up to 60% of the generated CO 2 from cement production. Combination of the limestone decomposition and thermal requirements of the clinkering process causes cement production to contribute 8-9% of annual global CO 2 emissions. Combustion of fossil fuels (coal, oil and gas) was shown to contribute a much larger portion of global CO 2 emissions. As of 2018, combustion of fossil fuels accounted for 65% of global CO 2 , where 41% was derived from stationary sources for electricity and heat generation and the other 24% was related to transport. To reduce these contributions, key steps forward in CO 2 utilization technologies are required. Therefore, a CO 2 mineralization technology (CO 2 mineralization concrete) to reduce the OPC content in concrete, while utilizing flue gas emissions from fossil fuel combustion has been developed to address both areas simultaneously. This Reversa™ technology utilizes low-carbon cementation agents produced by in situ CO 2 mineralization (“mineral carbonation reactions”) to offer a promising alternative to OPC. CO 2 mineralization relies upon the reaction of dissolved CO 2 with inorganic alkaline reactants to precipitate mineral carbonates (e.g., CaCO 3 ), which bind proximate particles and achieve cementation. Herein, a concrete green body, which is composed of a mixture of binder, water, and mineral aggregates, is exposed to CO 2 borne in industrial flue gas streams. This manner of CO 2 mineralization allows the production of construction components that feature equivalent engineering attributes as their OPC-based counterparts while featuring a much smaller embodied carbon intensity (eCI). The purpose of this project is to demonstrate the feasibility of the Reversa process evolving from a TRL-3 technology at the bench-scale up to TRL-6 technology at the pilot-scale. The reliability of the Reversa technology was tested to prove the effective production of three standard industrial concrete products selected during the course of the project. The results detailed herein will demonstrate the evolution of this technology to the industrial scale. The culmination of this work resulted in 9 production runs completed at the National Carbon Capture Center (NCCC), Wilsonville, AL, using natural gas (NG) flue gas as the CO 2 source. Over the course of the production runs at NCCC, the CO 2 utilization as a function of time, 24-h CO 2 uptake, electricity usage, and 28-d net area compressive strength recorded for each run. Collection of this data will be used to determine the success of the demonstration goals: (1) achieving in excess of 0.2gCO 2 /g reactant , (2) achieving greater than 50% reduction in global warming potential compared to standard produced units, and (3) ensuring compliance of carbonated concrete with industry standard specifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

MAESTRO, CASTRO, and SEDONA -- Petascale Codes for Astrophysical Applications

Performing high-resolution, high-fidelity, three-dimensional simulations of Type Ia supernovae (SNe Ia) requires not only algorithms that accurately represent the correct physics, but also codes that effectively harness the resources of the most powerful supercomputers. We are developing a suite of codes that provide the capability to perform end-to-end simulations of SNe Ia, from the early convective phase leading up to ignition to the explosion phase in which deflagration/detonation waves explode the star to the computation of the light curves resulting from the explosion. In this paper we discuss these codes with an emphasis on the techniques needed to scale them to petascale architectures. We also demonstrate our ability to map data from a low Mach number formulation to a compressible solver.

79 ASTRONOMY AND ASTROPHYSICS↗

Feasibility Analysis for the Use of Retrofitted Air-Conditioners Using Thermal Energy Storage (TES) for High Ambient Temperature (HAT) Countries

In high ambient temperature (HAT) countries, summer temperatures exceed 35℃, degrading the performance of air-conditioning systems and straining power grids. In this paper, a feasibility analysis was conducted to investigate potential savings during the peak using latent Thermal Energy Storage (TES) at near room phase-change temperatures, replacing condensers, thus, minimizing temperature lifts. Weather data, building loads, and baseline air-conditioning systems data were gathered for Dubai. A transient vapor-compression model in Modelica was used to compare the COP, total power input and cooling capacity of the air-conditioner at peak hours when ambient temperatures range between 35 – 45 ⁰C versus the TES-Phase-Change Material (PCM) melting temperatures from 22 – 28⁰C. The results indicate that the TES-PCM can enhance the system COP at the peak by a factor 1.4 and 2 for during for outdoor temperatures of 35 – 40⁰C, and 40 – 45⁰C, respectively. Lower melting temperature PCMs were able to reduce the required power input by 30-50%, with more savings occurring at higher temperature days. On the other hand, higher temperature PCMs enhancements were minimal especially at outdoor ambient temperatures ranging between 35 – 40⁰C. Improvements to the cooling capacity range from 8 – 18 % for the outdoor temperature range of 35 – 45 ⁰C. An economic analysis was conducted to find the potential saving in utility costs for 30%, 60%, and 90% of the space cooling demands of Dubai, and find the trade-off points between utility savings and cost of PCM-TES implementation. If the peak loads are to be shifted by 6 hours daily, the percentage utility savings for the city is 18%. Using estimated costs of the PCM-TES, ranging from $200-500/kWh, the daily load shifting hours were estimated to range from 4 hours at the lowest cost systems to 2.5 hours at the highest costs.

25 ENERGY STORAGE↗

An experimental investigation of flame and autoignition behavior of propane

We report autoignition delay time data are one important means to develop, quantify, and validate fundamental understanding of combustion chemistry at low temperatures (T<1200 K). However, low-temperature chemistry often has higher uncertainties and scatter in the experimental data compared with high-temperature ignition data (T>1200 K). In this study, autoignition properties of propane and oxygen mixtures were investigated using the University of Michigan rapid compression facility in order to understand the effects of ignition regimes on low-temperature ignition data. For the first time for propane, autoignition delay times were determined from pressure histories, and autoignition characteristics were simultaneously recorded using highspeed imaging of the test section through a transparent end-wall. Propane mixtures with fuel-to-O 2 equivalence ratios of φ = 0.25 and φ = 0.5 and O 2 -to-inert gas molar ratios of 1:3.76 were studied over the pressure range of 8.9 to 11.3 atm and the temperature range of 930 – 1070 K. The results showed homogeneous or strong autoignition occurred for all φ = 0.25 experiments, and inhomogeneous or mixed autoignition occurred for all φ = 0.5 experiments. While a limited temperature range is covered in the study, importantly the data span predicted transitions in autoignition behavior, allowing validation of autoignition regime hypotheses. Specifically, the results agree well with strong-autoignition limits proposed based on the Sankaran Criterion. The autoignition delay time data at the strong-ignition conditions are in excellent agreement with predictions using a well-validated detailed reaction mechanism from the literature and a zerodimensional modeling assumption. However, the experimental data at the mixed autoignition conditions were systematically faster than the model predictions, particularly at lower temperatures (T< ~970 K). The results are an important addition to the growing body of data in the literature that show mixed autoignition phenomena are important sources of the higher scatter observed in the low-temperature autoignition data for propane and other fuels. The results are discussed in terms of different methods to capture the effects of pre-autoignition heat release associated with mixed autoignition conditions and thereby address some of the discrepancies between kinetic modeling and experimental measurements.

42 ENGINEERING↗

Exact reconstruction of gene regulatory networks using compressive sensing

We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network’s sparseness. For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented. The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

59 BASIC BIOLOGICAL SCIENCES↗

Fuel convergence sensitivity in indirect drive implosions

In inertial confinement fusion experiments at the National Ignition Facility, a spherical shell of deuterium–tritium fuel is imploded in an attempt to reach the conditions needed for fusion, self-heating, and eventual ignition. Since theory and simulations indicate that ignition efficacy in 1D improves with increasing imploded fuel convergence ratio, it is useful to understand the sensitivity of the scale-invariant fuel convergence on all measurable or inferable 1D parameters. In this paper, we develop a simple isobaric and isentropic compression scaling model incorporating sensitivity to the in-flight adiabat inferred from shock strengths, to measured implosion velocity, and to known initial ablator and fuel aspect ratio and mass ratio. The model is first benchmarked to 1D implosion simulations spanning a variety of relevant implosion designs. We then use the model to compare compressibility trends across all existing indirect-drive layered implosion data from the facility spanning three ablators [CH, carbon (C), and Be], for which in-flight fuel adiabats varied from 1.6 to 5 by varying the number of drive shocks from 2 to 4, peak implosion velocities varied by 1.4×, capsule radii by 50%, and initial fuel aspect ratios by 1.4×. We find that the strength of the first shock is the dominant contributor setting the maximum fuel convergence. We also observe additional sensitivities to successive shock strengths and fuel aspect ratios that improve the agreement between the expected and measured compression for carbon and Be designs with adiabats above 3. A principal finding is that the adiabat 2.5 C-shell designs exhibit less convergence than CH-shell designs of similar inferred in-flight adiabat.

Landen, O. L. (ORCID:0000000214998217)↗

FY2025 Status Report: Model 9975 O Ring Fixture Long-Term Leak Performance

Leak testing experiments to monitor the aging performance of Viton® GLT and GLT-S O-rings used in the model 9975 shipping package have been ongoing since 2004 at Savannah River National Laboratory. Seventy tests using mock-up 9975 primary containment vessels (PCVs) with GLT O-rings were assembled and heated to temperatures ranging from 200 to 450 °F. Due to material substitution, fourteen tests with GLT-S O-rings were initiated in 2008 and heated to temperatures ranging from 200 to 400 °F. The conditioning temperatures are elevated compared to the calculated maximum O-ring temperature in a 9975 package in storage, 158 °F, to accelerate aging and enable observations of O-ring failures in a reasonable time frame. The mock-up PCV fixtures are leak tested periodically, and all GLT O-ring fixtures aged at 350 °F or above have failed to maintain a leak-tight seal. Eight GLT O-ring fixtures aged at 300 °F have failed after 2.8 to 5.7 years at temperature while the remaining fixtures at 300 °F were retired from testing following more than five years of aging without failure. Two of these retired fixtures were returned to testing and heated to 350 °F to evaluate the impact of additional heating at higher temperature for aged O-rings. Fixture #20 failed after 3 months while fixture #18 failed after 9 months at 350 °F. These O-rings demonstrated that aged and in-service O-rings can continue to be used, even at higher temperatures, after being in storage, and their leak performance are consistent with other samples at 350 °F. There has been one GLT O-ring fixture which failed after 13.4 years of aging at 200 °F. However, 20 other GLT O-rings aging at 200 °F have remained leak-tight for over 16.9 years and remain in test. There are two GLT O-ring fixtures at 270 °F; one fixture has failed after 12.9 years while the other fixture remains in test after 12.5 years. All GLT-S O-ring fixtures aged at 300 °F or above have failed their leak test. No failures have yet been observed in GLT-S O-ring fixtures aging at 250 °F for 14.9 years, while one GLT-S O-ring fixture failed after 12.4 years at 200 °F. The leak testing data to date suggest the GLT and GLT-S O-rings aging in the K-Area Complex (KAC) storage at temperatures of 158 °F might maintain a leak-tight seal for up to 59 years. Data from the O-ring fixtures are generally consistent with results from compression stress-relaxation testing and provide confidence in the predictive models based on those results. However, uncertainty exists in extrapolating these elevated temperature results to the lower temperatures of interest for normal storage in KAC. The collective data from these test efforts suggest the minimum O-ring service life at KAC normal storage conditions should be at least 34 years for GLT and GLT-S O-rings. Measurement of compression set in O-rings removed from failed fixtures, compared to that from KAC surveillance O-rings, indicate significant margin remains for O-rings still in service in 9975 packages in KAC. Aging and periodic leak testing will continue for the remaining 24 mock-up PCV fixtures.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

The thermal equation of state of (Mg, Fe)SiO 3 bridgmanite (perovskite) and implications for lower mantle structures

The high–pressure/high–temperature equation of state (EOS) of synthetic 13% Fe–bearing bridgmanite (Mg silicate perovskite) is measured using powder X–ray diffraction in a laser–heated diamond anvil cell with a quasi–hydrostatic neon pressure medium. We compare these results, which are consistent with previous 300 K sound speed and compression studies, with a reanalysis of Fe–free Mg end–member data from Tange et al. (2012) to determine the effect of iron on bridgmanite's thermoelastic properties. EOS parameters are incorporated into an ideal lattice mixing model to probe the behavior of bridgmanite at deep mantle conditions. With this model, a nearly pure bridgmanite mantle composition is shown to be inconsistent with density and compressibility profiles of the lower mantle. We also explore the buoyant stability of bridgmanite over a range of temperatures and compositions expected for Large Low–Shear Velocity Provinces, concluding that bridgmanite–dominated thermochemical piles are more likely to be passive dense layers externally supported by convection, rather than internally supported metastable domes. Here, the metastable dome scenario is estimated to have a relative likelihood of only 4–7%, given the narrow range of compositions and temperatures consistent with seismic constraints. If buoyantly supported, such structures could not have remained stable with greater thermal contrast early in Earth's history, ruling out formation scenarios involving a large concentration of heat producing elements.

58 GEOSCIENCES↗

Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression

Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.

97 MATHEMATICS AND COMPUTING↗

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parametrized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Here, our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output.

97 MATHEMATICS AND COMPUTING↗

Comparing field data using Alpert multi-wavelets

In this paper we introduce a method to compare sets of full-field data using Alpert tree-wavelet transforms. The Alpert tree-wavelet methods transform the data into a spectral space allowing the comparison of all points in the fields by comparing spectral amplitudes. The methods are insensitive to translation, scale and discretization and can be applied to arbitrary geometries. This makes them especially well suited for comparison of field data sets coming from two different sources such as when comparing simulation field data to experimental field data. We have developed both global and local error metrics to quantify the error between two fields. Additionally, we verify the methods on two-dimensional and three-dimensional discretizations of analytical functions. Furthermore, we then deploy the methods to compare full-field strain data from a simulation of elastomeric syntactic foam.

42 ENGINEERING↗

Networks of pipelines for gas with nonconstant compressibility factor: stationary states

For the management of gas transportation networks, it is essential to know how the stationary states of the system are determined by the boundary data. The isothermal Euler equations are an accurate pde-model for the gas flow through each pipe. A compressibility factor is used to model the nonlinear relationship between density and pressure that occurs in real gas in contrast to ideal gas. The gas flow through the nodes is governed by algebraic node conditions that require the conservation of mass and the continuity of the pressure. We examine networks that are described by arbitrary finite graphs and show that for suitably chosen boundary data, subsonic stationary states exist and are uniquely determined by the boundary data. Our construction of the stationary states is based upon explicit representations of the stationary states on each single pipe that can easily be evaluated numerically. We also use the monotonicity properties of these states as functions of the boundary data.

97 MATHEMATICS AND COMPUTING↗

Diffraction Measurement of Reaction Products in Shock Compressed TATB on the NIF

The shock-induced reaction of high explosives to gaseous and solid reaction products is a rapid, complex process. Understanding solid reaction product structure and formation kinetics is essential in determining the high-pressure equation of state of these multicomponent systems. We use the National Ignition Facility (NIF) to shock compress ~500-µm thick pressed powder high-explosive TATB samples to 70-135 GPa and collect in situ structural data on detonation byproducts using the TARDIS X-ray diffraction diagnostic. Velocimetry is used to record the transmitted compression wave profile, which is well described by Cheetah hydrocode simulations coupled with a reactive flow model, providing strong evidence of reaction in the TATB sample. While an unambiguous determination of the product phases was not possible owing to the low signal-to-noise quality of the diffraction signal, X-ray diffraction data of the product phases formed within the first 50 ns of the reaction is most consistent with a mixture of amorphous products and crystalline hexagonal diamond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tension-compression asymmetry in superelasticity of SrNi 2 P 2 single crystals and the influence of low temperatures

ThCr 2 Si 2 -type intermetallic compounds are known to exhibit superelasticity associated with structural transitions through lattice collapse and expansion. These transitions occur via the formation and breaking of Si-type bonds, respectively, under uniaxial loading along the [0 0 1] direction. Unlike most ThCr 2 Si 2 -type intermetallic compounds, which have either an uncollapsed tetragonal structure or a collapsed tetragonal structure, SrNi 2 P 2 possesses a third type of collapsed structured: a one-third orthorhombic structure, for which one expects the occurrence of unique structural transitions and superelastic behavior. In this study, uniaxial compression and tension tests were conducted on micron-sized SrNi 2 P 2 single crystalline columns at room temperature, 200 K, and 100 K, to investigate the influence of loading direction and temperature on the superelasticity of SrNi 2 P 2 . Experimental data and density functional theory calculations revealed the presence of tension-compression asymmetry in the structural transitions and superelasticity, as well as an asymmetry in their temperature dependence, due to the opposite superelastic process associated with compression (forming P-P bonds) and tension (breaking P-P bonds). Additionally, following thermodynamics, the observations suggest that this asymmetric superelasticity could lead to an opposite elastocaloric effect between compression and tension, which could be beneficial potentially in obtaining large temperature changes compared to conventional superelastic solids that show the same elastocaloric effect regardless of loading direction. Furthermore, these results provide an important fundamental insight into the structural transitions, superelasticity processes, and potential elastocaloric effects in SrNi 2 P 2 .

36 MATERIALS SCIENCE↗

Highly-scalable GPU-accelerated compressible reacting flow solver for modeling high-speed flows

Emerging supercomputing systems utilize a combination of central processing units (CPUs) and graphics processing units (GPUs) in an effort to reach exascale capabilities while minimizing the energy footprint of operating such systems. Such heterogeneous machines introduce new challenges for fluids solvers because the hardware architecture and operation of a GPU are fundamentally different from conventional CPUs. In this work, a general approach for efficient implementation of finite-volume based reacting flow solvers on such heterogeneous systems is presented. Three main challenges, namely, data access pattern, thread divergence, and thread safety, are addressed. Since compressible reacting flows require special methods to deal with chemical reactions, hyperbolic and nonlinear convection terms, and the presence of turbulence, specific algorithms that ensure GPU-based efficiency are developed. The approach is demonstrated on the widely available OpenFOAM open source software by modifying core algorithms for GPU accessibility. The scalability of the resulting solver, is demonstrated using practical test cases, including flow through a scramjet engine and the dynamics of a rotating detonation engine. Here, the solver provides near-ideal scaleup on a large number of GPUs (>3000), and extremely efficient use of the GPUs, with throughput nearly a constant even when processing a large number of control volumes.

42 ENGINEERING↗

Genetic algorithm-based geometry calibration for dynamic compression x-ray diffraction experiments

An important component of dynamic compression x-ray diffraction (XRD) experiment analysis is geometry calibration: proper data interpretation requires knowledge of the precise detector position and orientation and, if the experiment involves a single-crystal sample, knowledge of the lattice orientation. The determination of these parameters in the arbitrary three-dimensional (3D) scattering geometries often present in dynamic compression facilities is challenging, as the associated optimization problem can be highly nonlinear, nonsmooth, and discontinuous. We present a genetic algorithm-based approach for performing dynamic compression XRD calibrations that overcomes these obstacles. We provide details regarding the image processing, algorithm implementation, and open-source software deployment and demonstrate the capability of the approach to calibrate the detector and crystal parameters in 3D geometries. Notably, we demonstrate the solver’s capacity to find the crystal orientation without a priori rotation constraints.

Brown, Nathan P. [Sandia National Laboratories (SN↗

Quantum Time Dynamics Mediated by the Yang–Baxter Equation and Artificial Neural Networks

Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang–Baxter equation (YBE). Unlike traditional error mitigation methods, which are computationally intensive, we investigate artificial error mitigation. We developed a novel method that combines ANNs for noise mitigation combined with the YBE to generate noisy data. This approach effectively reduces noise in quantum simulations, enhancing the accuracy of the results. The YBE rigorously preserves quantum correlations and symmetries in spin chain simulations in certain classes of integrable lattice models, enabling effective compression of quantum circuits while retaining linear scalability with the number of qubits. This compression facilitates both full and partial implementations, allowing the generation of noisy quantum data on hardware alongside noiseless simulations using classical platforms. By introducing controlled noise through the YBE, we enhance the data set for error mitigation. We train an ANN model on partial data from quantum simulations, demonstrating its effectiveness in mitigating errors in time-evolving quantum states, providing a scalable framework to enhance quantum computation fidelity, particularly in noisy intermediate-scale quantum (NISQ) systems. We demonstrate the efficacy of this approach by performing quantum time dynamics simulations using the Heisenberg XY Hamiltonian on real quantum devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗