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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Hybridizing pseudo-Hamiltonians and non-local pseudopotentials in diffusion Monte Carlo

An accurate treatment of effective core potentials (ECPs) requires care in continuum quantum Monte Carlo (QMC) methods. While most QMC studies have settled on the use of familiar non-local (NL) pseudopotentials with additional localization approximations, these approaches have been shown to result in moderate residual errors for some classes of molecular and solid state applications. Here, we revisit an idea proposed early in the history of QMC ECPs that does not require localization approximations, namely, a differential class of potentials referred to as pseudo-Hamiltonians. We propose to hybridize NL potentials and pseudo-Hamiltonians to reduce residual non-locality of existing potentials. We derive an approach to recast pseudopotentials for 3d elements as hybrid pseudo-Hamiltonians with optimally reduced NL energy. We demonstrate the fidelity of the hybrid potentials by studying atomic ionization potentials of Ti and Fe and the binding properties of TiO and FeO molecules with diffusion Monte Carlo (DMC). We show that localization errors have been reduced relative to potentials with the same NL channels for Sc–Zn by considering the DMC energy change with respect to the choice of approximate localization. While localization error decreases proportionate to the reduced NL energy without a Jastrow, with a Jastrow, the degree of reduction decreases at higher filling of the d-shell. Our results suggest that a subset of existing ECPs may be recast in this hybrid form to reduce the DMC localization error. They also point to the prospect of further reducing this error by generating ECPs within this hybrid form from the start.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Coalescence of GaP on V-Groove Si

With an increase of control over crystalline defects, metallorganic vapor phase epitaxy (MOVPE)-grown III-V-on-Si multijunction solar cells have seen rapid increases in efficiency in recent years, pointing to a promising path to lower cost III-V solar cells. However, the cost of chemo-mechanical polishing the Si wafers to prepare them for epitaxy is high. The use of V-groove nanopatterns enables similar defect reduction to that achieved on planar wafers, but the nanopatterns can be fabricated with a low-cost process. While V-grooves offer advantages over planar Si, they add complexity to the growth process. In particular, coalescence can cause the formation of threading dislocations, and the highly-directional growth conditions required for coalescence are unusual for MOVPE. We have studied the coalescence of GaP films nucleated directly on V-groove Si by MOVPE. We observed that for optimized growth conditions (V/III=5,000 and T=800 C) two growth modes were possible, and the resulting morphology depended on the exact geometry of the SiNx cap used to cover the (0 0 1)-oriented Si at the tops of the grooves. For caps with a width >100 nm, noncoalescing, nano ridge-like growth terminating in f1 1 1g facets was observed. For narrower caps, coalescence with an RMS roughness of 0.2 nm as measured by atomic force microscopy was observed. We will discuss mechanisms responsible for this phenomenon, including the role of Si from the substrate surface. The dislocation dynamics of this system were studied with electron channeling contrast imaging (ECCI) and transmission electron microscopy (TEM). We find that V-grooves do not block dislocation glide; ECCI measurements show misfit dislocations greater than 10 micrometers long observed to continue perpendicularly across neighboring V-grooves. In addition, all threading dislocations appear to lie on f1 1 1g planes, consistent with the GaP relaxing via glided-in glissile dislocations. The dislocation dynamics and morphological evolution of the coalescence of GaP on Si, possible mechanisms behind the observed phenomenons, and further dislocation mitigation strategies for these materials will be presented.

dislocation dynamics↗
DOE OSTI2021

Visualizing plant cell wall changes proves the superiority of hydrochloric acid over sulfuric acid catalyzed γ-valerolactone pretreatment

Thermochemical pretreatment is one of the key steps to process lignocellulosic biomass for production of biofuels and biomaterials. γ-valerolactone (GVL) has been used as a green and renewable solvent to efficiently dissolve lignin under acidic conditions and enhance subsequent enzyme digestibility. Further improvement is still required to lower the pretreatment temperature in order to reduce sugar degradation and irreversible lignin condensation, as well as the capital cost. In this study, we compared the use of HCl and H 2 SO 4 as catalysts during GVL pretreatment, and found the performance of GVL-HCl at 100 °C was comparable to that of GVL-H 2 SO 4 at 120 °C in terms of xylan and lignin removal and enzyme digestibility. We further monitored the lignin removal and cellulose accessibility in the cell walls at different pretreatment time points by imaging the changes in lignin auto-fluorescence and in CtCBM3-GFP binding with confocal laser scanning microscopy (CLSM), respectively. We found GVL-HCl pretreatment at a relative low temperature (100 °C) could rapidly remove lignin in the compound middle lamella and cell corner areas, which concurred with the increase of CtCBM3-GFP binding in these areas. Real-time imaging of cell wall degradation by cellulases further revealed that the secondary cell walls could be digested from both cell lumen and CML sides, and eventually fully deconstructed within 24 h. Furthermore, our results provide new insight into the effects of chloride anions on plant cell wall structure during GVL pretreatment, and offer potential routes to further optimize and enhance the efficiency of GVL-based pretreatment.

09 BIOMASS FUELS↗
DOE OSTI2022

A learned closure method applied to phase mixing in a turbulent gradient-driven gyrokinetic system in simple geometry

We present a new method for formulating closures that learn from kinetic simulation data. We apply this method to phase mixing in a simple gyrokinetic turbulent system – temperature-gradient-driven turbulence in an unsheared slab. The closure, called the learned multi-mode (LMM) closure, is constructed by, first, extracting an optimal basis from a nonlinear kinetic simulation using singular value decomposition. Subsequent nonlinear fluid simulations are projected onto this basis and the results are used to formulate the closure. We compare the closure with other closures schemes over a broad range of the relevant two-dimensional parameter space (collisionality and gradient drive). We find that the turbulent kinetic system produces phase-mixing rates much lower than the linear expectations, which the LMM closure is capable of capturing. We also compare radial heat fluxes. A Hammett–Perkins closure, generalized to include collisional effects, is quite successful throughout the parameter space, producing ${\sim }14\,\%$ root-mean-square (r.m.s.) error. The LMM closure is also very effective: when trained at three (two) points (in a 35 point parameter grid), the LMM closure produces $8\,\%$ ( $12\,\%$ ) r.m.s. errors. The LMM procedure can be readily generalized to other closure problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Mechanisms of Hysteresis and Reversibility across the Voltage-Driven Perovskite–Brownmillerite Transformation in Electrolyte-Gated Ultrathin La 0.5 Sr 0.5 CoO 3−δ

Perovskite cobaltites have emerged as archetypes for electrochemical control of materials properties in electrolyte-gate devices. Voltage-driven redox cycling can be performed between fully oxygenated perovskite and oxygen-vacancy-ordered brownmillerite phases, enabling exceptional modulation of the crystal structure, electronic transport, thermal transport, magnetism, and optical properties. The vast majority of studies, however, have focused heavily on the perovskite and brownmillerite end points. In contrast, here we focus on hysteresis and reversibility across the entire perovskite ↔ brownmillerite topotactic transformation, combining gate-voltage hysteresis loops, minor hysteresis loops, quantitative operando synchrotron X-ray diffraction, and temperature-dependent (magneto)transport, on ion-gel-gated ultrathin (10-unit-cell) epitaxial La 0.5 Sr 0.5 CoO 3-δ films. Further, gate-voltage hysteresis loops combined with operando diffraction reveal a wealth of new mechanistic findings, including asymmetric redox kinetics due to differing oxygen diffusivities in the two phases, nonmonotonic transformation rates due to the first-order nature of the transformation, and limits on reversibility due to first-cycle structural degradation. Minor loops additionally enable the first rational design of an optimal gate-voltage cycle. Combining this knowledge, we demonstrate state-of-the-art nonvolatile cycling of electronic and magnetic properties, encompassing >10 5 transport ON/OFF ratios at room temperature, and reversible metal-insulator-metal and ferromagnet-nonferromagnet-ferromagnet cycling, all at 10-unit-cell thickness with high room-temperature stability. This paves the way for future work to establish the ultimate cycling frequency and endurance of such devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Record high room temperature resistance switching in ferroelectric-gated Mott transistors unlocked by interfacial charge engineering

Abstract The superior size and power scaling potential of ferroelectric-gated Mott transistors makes them promising building blocks for developing energy-efficient memory and logic applications in the post-Moore’s Law era. The close to metallic carrier density in the Mott channel, however, imposes the bottleneck for achieving substantial field effect modulation via a solid-state gate. Previous studies have focused on optimizing the thickness, charge mobility, and carrier density of single-layer correlated channels, which have only led to moderate resistance switching at room temperature. Here, we report a record high nonvolatile resistance switching ratio of 38,440% at 300 K in a prototype Mott transistor consisting of a ferroelectric PbZr 0.2 Ti 0.8 O 3 gate and an R NiO 3 ( R : rare earth)/La 0.67 Sr 0.33 MnO 3 composite channel. The ultrathin La 0.67 Sr 0.33 MnO 3 buffer layer not only tailors the carrier density profile in R NiO 3 through interfacial charge transfer, as corroborated by first-principles calculations, but also provides an extended screening layer that reduces the depolarization effect in the ferroelectric gate. Our study points to an effective material strategy for the functional design of complex oxide heterointerfaces that harnesses the competing roles of charge in field effect screening and ferroelectric depolarization effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Efficient conversion of syngas to linear α-olefins by phase-pure χ-Fe5C2

Abstract Oil has long been the dominant feedstock for producing fuels and chemicals, but coal, natural gas and biomass are increasingly explored alternatives 1–3 . Their conversion first generates syngas, a mixture of CO and H 2 , which is then processed further using Fischer–Tropsch (FT) chemistry. However, although commercial FT technology for fuel production is established, using it to access valuable chemicals remains challenging. A case in point is linear α-olefins (LAOs), which are important chemical intermediates obtained by ethylene oligomerization at present 4–8 . The commercial high-temperature FT process and the FT-to-olefin process under development at present both convert syngas directly to LAOs, but also generate much CO 2 waste that leads to a low carbon utilization efficiency 9–14 . The efficiency is further compromised by substantially fewer of the converted carbon atoms ending up as valuable C 5 –C 10 LAOs than are found in the C 2 –C 4 olefins that dominate the product mixtures 9–14 . Here we show that the use of the original phase-pure χ-iron carbide can minimize these syngas conversion problems: tailored and optimized for the process of FT to LAOs, this catalyst exhibits an activity at 290 °C that is 1–2 orders higher than dedicated FT-to-olefin catalysts can achieve above 320 °C (refs. 12–15 ), is stable for 200 h, and produces desired C 2 –C 10 LAOs and unwanted CO 2 with carbon-based selectivities of 51% and 9% under industrially relevant conditions. This higher catalytic performance, persisting over a wide temperature range (250–320 °C), demonstrates the potential of the system for developing a practically relevant technology.

Science & Technology - Other Topics↗
DOE OSTI2021

Calculation of permanent magnet arrangements for stellarators: a linear least-squares method

A problem arising in several engineering areas is to design magnets outside a volume that produce a desired magnetic field inside it. One instance of this problem is stellarator design, where it has recently been shown that permanent magnets can provide the required shaping of the magnetic field. Here we demonstrate a robust and efficient algorithm REGCOIL_PM to calculate the spatial distribution of these permanent magnets. The procedure involves a small number of fixed-point iterations, with a linear least-squares problem solved at each step. Furthermore, the method exploits the Biot–Savart Law's exact linearity in magnetization density and approximate linearity in magnet size, for magnets far from the target region. No constraint is placed on the direction of magnetization, so Halbach solutions are found naturally, and the magnitude of the magnetization can be made uniformly equal to a target value.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2023

Abstract for CRADA between NETL and the University of Massachusetts Lowell (AGMT-1275)

The demand for printed electronics over conventional electronics is growing each year due to their low costs, low material waste during manufacturing, and compatibility with flexible substrates. The development of inks for printed electronics is a challenge because each printing technology requires different viscosities and curing behaviors to create devices with controlled structures at the micron scale, or smaller. Carbon is a great candidate for fabricating printed electronics devices because it is inexpensive and earth abundant, easily functionalized to impart miscibility with solvent systems, and has electrical properties that can be tuned from insulating to conducting. Despite these advantages, carbons are rarely used for printed electronics because there is a scarcity of inks available for non-contact printing technologies. To address these challenges, the National Energy Technology Laboratory (NETL) will collaborate with the University of Massachusetts Lowell (Participant) through its Printed Electronics Research Collaborative (PERC) to develop engineered carbons and ink formulations optimized specifically for printing radio- and microwave-frequency electronic devices using ink jet, aerosol jet, syringe dispensing, and other additive manufacturing techniques. NETL will process coal, petroleum, and other carbon feedstocks to make engineered carbons with the appropriate composition, microstructure, surface functionalization, and particle morphologies needed to stabilize ink suspensions and impart useful electronic properties to printed devices. The University of Massachusetts Lowell will utilize these engineered carbons to determine which solvent systems and ink additives are needed to achieve the appropriate viscosity, stability, and printability to make useful ink formulations. The University of Massachusetts Lowell will then utilize these inks to manufacture printed resistors and conductors using inkjet, aerosol jet, and syringe dispensing printing methods and will characterize device performance using four-point probe resistivity measurements, contact angle measurements to assess wettability, adhesion evaluation to various substrates, and focused ion beam and scanning electron microscopy for surface and density investigations of printed devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Discovery and Structure-Based Optimization of Potent and Selective WD Repeat Domain 5 (WDR5) Inhibitors Containing a Dihydroisoquinolinone Bicyclic Core

WD repeat domain 5 (WDR5) is a member of the WD40-repeat protein family that plays a critical role in multiple chromatin-centric processes. Overexpression of WDR5 correlates with a poor clinical outcome in many human cancers, and WDR5 itself has emerged as an attractive target for therapy. Most drug-discovery efforts center on the WIN site of WDR5 that is responsible for the recruitment of WDR5 to chromatin. In this paper, we describe discovery of a novel WDR5 WIN site antagonists containing a dihydroisoquinolinone bicyclic core using a structure-based design. These compounds exhibit picomolar binding affinity and selective concentration-dependent antiproliferative activities in sensitive MLL-fusion cell lines. Furthermore, these WDR5 WIN site binders inhibit proliferation in MYC-driven cancer cells and reduce MYC recruitment to chromatin at MYC/WDR5 co-bound genes. Thus, these molecules are useful probes to study the implication of WDR5 inhibition in cancers and serve as a potential starting point toward the discovery of anti-WDR5 therapeutics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Development of process parameters and post-build conditions for qualification of LPBF 316 SS

To harness the potential of laser powder bed fusion (LPBF) 316H stainless steel (SS) for use in advanced nuclear reactors, extensive research efforts are needed to develop optimal laser processing parameters and appropriate heat treatments, taking into account various manufacturing platforms and locations. Furthermore, it is crucial to assess the material properties in environments relevant to reactor operating conditions. In collaboration with Oak Ridge National Laboratory (ORNL) and Los Alamos National Laboratory (LANL), Argonne National Laboratory (ANL) is dedicated to gaining insights into how the manufacturing process and post-build treatments impact the performance of LPBF 316H SS. This report provides an overview of ANL's research findings for FY23, focusing on LPBF 316H SS produced using a Renishaw AM400 laser system. Specifically, our research has concentrated on three key areas: 1) Optimum laser processing parameters: Leveraging the high-throughput printing technology, we systematically varied the laser power, exposure time and point distance within the same build. By measuring the as-printed porosity, we identified an optimum printing parameter window that produced materials with near-complete density; 2) Post-built treatment development: Materials printed with the optimum laser parameters underwent treatments including stress relief, solution annealing, and hot isostatic pressing. Subsequently, we fabricated tension, creep, and fatigue specimens from materials subjected to these different conditions. Over the next year, a series of scoping tests will be conducted to facilitate the selection of the most suitable post-build treatment condition; 3) Thermal aging effects on microstructure and properties: thermal aging at 550°C, 650°C and 750°C was conducted on LPBF 316H SS specimens up to 2500 h. Microstructural characterization was performed with electron microscopy, and the evolution of the dislocation cell structures and secondary phases was studied. Microhardness tests and tension tests were conducted to quantify changes in mechanical properties. The results revealed that the aged LPBF 316H SS exhibited a high density of Cr-rich and Mo-rich fine precipitates within grains due to the presence of a high density of dislocations and the globally distributed dislocation cell structures, which served as heterogeneous nucleation sites for secondary phases. Along grain boundaries, a Mo-Cr-rich phase, observed in 750°C-aged conditions, displayed significant thermal coarsening. Throughout the aging process, various mechanisms, including stress relaxation, dislocation cell structure recovery, solution hardening, and precipitation hardening, competed to influence materials strength. These results contribute towards establishing the technical basis for qualifying LPBF 316H SS for advanced nuclear reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Virtual Tokamak for Test and Development of Plasma Control Applied to NSTX-U

Safe and efficient operation of tokamak experiments depends on shot preparation prior to the experiment to maximize performance and avoid issues that are predictable consequences of known physics. The General Atomics TokSys toolbox is used to test and develop the plasma control system (PCS) on numerous tokamaks around the world. Tokamaks that use a version of the DIII-D PCS can connect it to a TokSys simulation and control a virtual version of the tokamak with the real PCS. Recent upgrades to the simulation include more detailed profile and transport modeling to support design and implementation of optimized scenarios. The simulation combines a module called “Profiles” that simulates the 1D profiles of density, pressure, and current and a module called “GSevolve'' that evolves the 2D Grad-Shafranov equilibrium. The “Profiles'' module primarily uses simplified models for fueling, heating, and current. The TRANSP code provides precomputed diffusion coefficients and heating/current drive profiles from radio-frequency and neutral beam sources for the scenarios being simulated. The fidelity of a simulation can be enhanced by analyzing it with TRANSP and making corrections to the precomputed values and iterating until convergence. Simulations of the National Spherical Torus Upgrade (NSTX-U) are presented. It is shown that the simulation reproduces the experimental profile evolution. Here, the simulation has also been upgraded to reproduce the experimental outcome at points of bifurcation. This is demonstrated with a simulation of vertical displacement in NSTX-U.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Investigation of oxygen ion mobility through Zr x Ta 2 O 2x+5 (ZTOx) and Hf 6 Ta 2 O 17 (HTO) at elevated temperatures

Thermal Protection Systems (TPS) allow for technology to withstand high heat, which is especially critical for aerospace applications. As technology advances, better TPS are required to keep up with more extreme operating temperatures. Ultra-High Temperature Ceramics (UHTCs) are of interest because of their high melting points above 3000°C, but they suffer from oxidation when exposed to the atmosphere. A standard remedy to prevent oxidation is the use an oxide coating to shield from oxygen in the atmosphere while still allowing high operating temperatures and thermal cycling. However, standard oxide coatings such as yttria stabilized zirconia (YSZ) have limited operating temperatures before they allow oxygen to diffuse into the substrate material. A recently proposed alternative material has the formula A 6 B 2 O 17 (A: Zr or Hf) (B: Ta or Nb). In this study, determination of the oxygen protection ability of Hf 6 Ta 2 O 17 (HTO), Zr 6 Ta 2 O 17 (ZTO6), and various Zr x Ta 2 O 2x+5 (ZTOx) was determined using Electrochemical Impedance Spectroscopy (EIS). Optimal synthesis and sintering conditions were determined for ZTO6 and several of its ZTO variations. Activation energy for oxygen mobility was also determined. The importance of ball milling was found to be crucial to achieve high sintering density with these ceramics. The sintering study revealed that the ideal temperature of the ZTO variations increased with increased Zr/Ta ratio in the formulation. It was determined that both HTO and the ZTO variations conduct oxygen ions less than current ceramic coatings such as YSZ, with HTO conducting the least, with results confirming the validity of both HTO and ZTO materials as candidates for TPS coatings based on their low oxygen ion conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

It has been shown that model predictive control (MPC) is a promising solution for energy-efficient building operations. However, the deployment of MPC in a large portion of the building stock has not been possible partially because of high installation costs. Every building is unique and requires a tailored MPC solution. The best performing solutions are often based on physics-based modeling, which is, however, computationally expensive and requires dedicated software. A promising direction that tackles this problem is to train a neural network-based optimal control policy to imitate the behavior of physics-based MPC from the simulation data generated offline. The neural networks give control actions that closely approximate those produced by physics-based MPC, but with a fraction of the computational and memory requirements and without the need for licensed software. The main advantage of the proposed approach stems from simple evaluation at execution time, leading to low computational footprints and easy deployment on embedded HW platforms. In the case study, we present the energy savings potential of physics-based MPC applied to an office building in Belgium. We demonstrate how neural network approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the remaining challenges and open research questions.

Drgona, Jan↗
DOE OSTI2024

A General Framework for Error-controlled Unstructured Scientific Data Compression

Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework designed to enhance the compression of floating-point unstructured mesh data, which is common in scientific applications. Our approach involves interpolating mesh data onto a rectilinear grid and then separately compressing the grid interpolation and the interpolation residuals. This method is general, independent of mesh types and typologies, and can be seamlessly integrated with existing lossy compressors for improved performance. We evaluated our framework across twelve variables from two synthetic datasets and two real-world simulation datasets. The results indicate that the multi-component framework consistently outperforms state-of-the-art lossy compressors on unstructured data, achieving, on average, a 2.3 − 3.5× improvement in compression ratios, with error bounds ranging from 1 × 10 the −6 to 1×10−2. We further investigate impact of hyperparameters, such as grid spacing and error allocation, to deliver optimal compression ratios in diverse datasets.

Gong, Qian↗