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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Synthetic Transcription Factor and Core Promoter System in Picochlorum renovo Enables Tunable Gene Expression

Picochlorum renovo is a recently characterized microalga of industrial interest. Its rapid growth rate, and high temperature and salinity tolerances make P. renovo an attractive candidate for industrial scale cultivation and downstream production of sustainable fuels and chemicals. Currently, genetic tools for many non-model microalgae are limited and would greatly benefit from an orthogonal gene expression system to bypass host regulation. Additionally, the engineering of complex metabolic pathways in eukaryotic organisms to optimize growth or biosynthesize high value products often requires tunable expression of each gene in a pathway. Here we explore a tunable orthogonal gene expression system using a synthetic transcription factor (sTF) and core promoters (CPs) conferring expression of the fluorescent protein mCherry to quantify protein expression. The sTF paired with the relevant binding site (BS) led to an ~5X increase in reporter gene expression compared to the native RuBisCo promoter, however had limited tunability with increasing BS number. Quantification of mCherry expression under 34 different CPs paired with the sTF and BS showed an order of magnitude of expression tunability. Future work with this system will entail generation of an overexpression library via random integration of the relevant BS in an sTF expressing P. renovo strain. With this sTF and CP system we aim to greatly improve growth rates and product titers in photosynthetic organisms, while also providing a potentially universal gene expression system for microalgae.

algae↗
DOE OSTI2024

A new generation of effective core potentials: Selected lanthanides and heavy elements

Here, we construct correlation-consistent effective core potentials (ccECPs) for a selected set of heavy atoms and ƒ-elements that are of significant current interest in materials and chemical applications, including Y, Zr, Nb, Rh, Ta, Re, Pt, Gd, and Tb. As is customary, ccECPs consist of spin orbit averaged relativistic effective potential (AREP) and effective spin-orbit (SO) terms. For the AREP part, our constructions are carried out within a relativistic coupled-cluster framework while also taking into account objective function one-particle characteristics for improved convergence in optimizations. The transferability is adjusted using binding curves of hydride and oxide molecules. We address the difficulties encountered with ƒ-elements, such as the presence of large cores and multiple near-degeneracies of excited levels. For these elements, we construct ccECPs with core valence partitioning that includes 4ƒ-subshell in the valence space. The developed ccECPs achieve an excellent balance between accuracy, size of the valence space, and transferability and are also suitable to be used in plane wave codes with reasonable energy cutoffs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A Comparative Study for Optimization of Sensitivity and Noise Levels in Race-Track Sensors

In this study, we have designed fluxgate sensors at race-track geometry which were used to measure DC or low-frequency magnetic fields. The type of core material and the number of core layers on the sensitivity and the noise level were specifically examined. We could achieve to get a sensitivity of 440 kV/T and a noise level of 56 pT/ √Hz at 1 Hz in a large dynamical range extending up to 1.5 Oe.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Core–shell PdPb@Pd aerogels with multiply-twinned intermetallic nanostructures: facile synthesis with accelerated gelation kinetics and their enhanced electrocatalytic properties

Delicately engineering well-defined noble metal aerogels with favorable structural and compositional features is of vital importance for wide applications. Here, we reported a one-pot and facile method for synthesizing core–shell PdPb@Pd hydrogels/aerogels with multiply-twinned grains and an ordered intermetallic phase using sodium hypophosphite as a multifunctional reducing agent. Due to the accelerated gelation kinetics induced by increased reaction temperature and the specific function of sodium hypophosphite, the formation of hydrogels can be completed within 4 h. As a result, owing to their unique porous structure and favorable geometric and electronic effects, the optimized PdPb@Pd aerogels exhibit enhanced electrochemical performance towards ethylene glycol oxidation with a mass activity of 5.8 times higher than Pd black.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Directed surfaces structures and interfaces for enhanced electrocatalyst activity, selectivity, and stability for energy conversion reactions

In this project, we have employed a systematic approach to develop active, selective, and stable catalyst materials for important electrochemical reactions involving energy conversion. In particular, we have focused our attention on developing active catalyst materials for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). HER: We have synthesized and investigated several highly active and acid stable non-precious metal HER catalysts, including: [Mo 3 S 13 ] 2- nanoclusters (Nature Chemistry, 2014) and molybdenum phosphosulfide (MoP|S) (Angewandte Chemie, 2014). We have also aimed to engineer these catalyst formulations in a membrane electrode assembly (MEA) for fundamental studies of water electrolysis at high current densities, approximately 1 A/cm 2 (ChemSusChem, 2015). We furthermore investigated transition metal phosphide (TMP) catalysts for HER by a combined experimental–theoretical approach (Energy & Environmental Science, 2015). By synthesizing different TMPs and comparing experimentally determined HER activities with the hydrogen adsorption free energies, ΔG H , calculated by density functional theory, we showed that the TMPs follow a volcano relationship for the HER. Using our combined experimental–theoretical model, we predicted that the mixed metal TMP, Fe 0.5 Co 0.5 P, should have a near-optimal ΔG H . We synthesized several mixtures of Co and Fe phosphides alloys and confirmed that Fe 0.5 Co 0.5 P exhibits the highest HER activity of the investigated TMPs (Energy & Environmental Science, 2015). The understanding gained as to how to improve catalytic activity for the HER, particularly for non-precious metal materials, is important to DOE targets for sustainable H 2 production. OER: We have developed a SrIrO 3 /IrO x catalyst for acidic conditions (submitted, 2016). The SrIrO 3 /IrO x catalyst significantly outperforms rutile IrO 2 and RuO 2 , the only other OER catalysts to have reasonable stability and activity in acidic electrolyte, and in fact demonstrates the best activity for any known OER catalyst measured in either acidic or in alkaline electrolyte. For alkaline conditions we have demonstrated that the combined effect of cerium as a dopant and gold as a metal support, significantly enhances the OER activity of electrodeposited NiO x films. This NiCeO x -Au catalyst delivers high OER activity in alkaline media, and is among the most active OER electrocatalysts reported to date (Nature Energy, accepted 2016). These studies of new catalysts for the OER, both in acid and in base, are fundamental to enabling new technologies of interest for the DOE, including the production of sustainable fuels and chemicals. ORR: One method to significantly reduce the Pt loading in fuel cell devices is to increase the ORR activity of Pt based systems. To this end we have synthesized a high surface area supported meso-structured Pt x Ni alloy thin film with a double gyroid morphology that both exhibits high activity and stability for the ORR (submitted, 2016). We have furthermore developed a Ru-core, Pt-shell system that improves the per Pt site activity by more than a factor of 2 (ChemElectroChem, 2014). Further refinement, optimizing Pt-shell thickness and reducing particle sintering during processing, enabled us to obtain a mass activity that is 2 times higher than commercial Pt/C from TKK. These are important contributions to the DOE goal of reducing Pt loading since an improved understanding of how to increase mass activity and stability helps enable low Pt content fuel cells.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Excited states via coupled cluster theory without equation-of-motion methods: Seeking higher roots with application to doubly excited states and double core hole states

Here, in this work, we revisited the idea of using the coupled-cluster (CC) ground state formalism to target excited states. Our main focus was targeting doubly excited states and double core hole states. Typical equation-of-motion (EOM) approaches for obtaining these states struggle without higher-order excitations than doubles. We showed that by using a non-Aufbau determinant optimized via the maximum overlap method, the CC ground state solver can target higher energy states. Furthermore, just with singles and doubles (i.e., CCSD), we demonstrated that the accuracy of ΔCCSD and ΔCCSD(T) (triples) far surpasses that of EOM-CCSD for doubly excited states. The accuracy of ΔCCSD(T) is nearly exact for doubly excited states considered in this work. For double core hole states, we used an improved ansatz for greater numerical stability by freezing core hole orbitals. The improved methods, core valence separation (CVS)-ΔCCSD and CVS-ΔCCSD(T), were applied to the calculation of the double ionization potential of small molecules. Even without relativistic corrections, we observed qualitatively accurate results with CVS-ΔCCSD and CVS-ΔCCSD(T). Remaining challenges in ΔCC include the description of open-shell singlet excited states with the single-reference CC ground state formalism as well as excited states with genuine multireference character. The tools and intuition developed in this work may serve as a stepping stone toward directly targeting arbitrary excited states using ground state CC methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Geochemistry & Geophysics↗
DOE OSTI2026

Accelerating laser ray tracing in high fidelity physics simulations of laser melting using squeeze U-net

Laser melting is a core component of the ongoing industrial revolution, dubbed Industry 4.0, as lasers facilitate fast and precise melting and fusion in advanced manufacturing. There is a strong need to optimize the laser process using simulations. However, this has proven challenging as high fidelity simulations are needed for predictive modeling and this is currently prohibitively expensive even when run on hundreds of processors on high performance computers. The challenge is capturing complex physics of laser material interaction, fluid dynamics, thermal physics and material phase transformations at various length and time scales. To close this technological gap, we modified a squeeze U-net to accelerate the laser ray tracing component of such high fidelity models by ~4x–40x while preserving the core physics principle of conservation of energy with 97% accuracy. This approach enables the accurate modeling of global laser energy absorption as a function of local surface temperatures and complex surface topologies, which govern the reflection directions and energy losses of laser rays upon interacting with the material surface.

Computer science↗
DOE OSTI2025

HFIR LEU High Density Silicide Dispersion Optimized Design Neutronics Analyses with PHAME

A high-fidelity neutronics model of the Oak Ridge National Laboratory High Flux Isotope Reactor (HFIR) with the low-enriched uranium (LEU) high-density silicide dispersion Optimized fuel design was updated and analyzed to generate reactor physics-based metrics to support follow-on thermal hydraulic and transient analyses of this design. The Python HFIR Analysis and Measurement Engine (PHAME) was also updated to enhance the automation capabilities of the framework developed and maintained to perform these reactor physics modeling and simulation efforts. The automated framework significantly increases the efficiency and reproducibility to design and thoroughly analyzes HFIR LEU core designs, changes, and uncertainties. Reactor physics metrics evaluated include but are not limited to fuel depletion, cycle length, fission rate density distributions, axial power peaking factors, kinetics data, reactivity coefficients, control element worths, heat deposition rates, and decay heat. These neutronics results provide essential input to follow-on steady state thermal, thermal hydraulic and reactor transient analyses, which are subject of other reports. The Optimized design operates at 95 MW to maintain HFIR’s current highly enriched uranium core performance level at 85 MW.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

DGaaS: GPU as a Service on Distributed Computing System

In the rapidly evolving landscape of scientific computing, Graphics Processing Units (GPUs) have become indispensable for their unparalleled ability to handle parallel tasks in complex calculations, simulations, and data analysis. Their utility is further magnified in machine learning and AI applications, where they significantly accelerate model training and predictive analytics. Within this context, the Triton Inference Server emerges as a pivotal open-source tool, specializing in AI inferencing and optimizing GPU utilization across various platforms and frameworks. This paper presents an in-depth study on distributed High Throughput Computing (HTC), specifically focusing on the HTCondor framework and its resource provisioning tools, GlideinWMS and HEPCloud. These systems enable large-scale scientific experiments like CMS and DUNE to efficiently access and utilize vast computational resources. The paper explores the core architectural components of GlideinWMS, including jobs, user pools, and worker nodes, and discusses their integration with GPUs and the Triton server. The primary aim of this research is to develop a solution that optimizes GPU utilization by leveraging Glideins and containers. This approach allows computational jobs, particularly those involving AI models, to use GPUs only when essential, thereby facilitating efficient sharing of limited GPU resources. To validate this architecture, the study conducted three key tests involving custom scripts, container-based servers, and Triton server deployments. However, the study faces challenges, notably in locating the Triton server and ensuring secure remote access. To address these issues, future work will focus on developing a proxy mechanism and enhancing security protocols. In conclusion, this study offers a comprehensive roadmap for effective and efficient GPU utilization in distributed High Throughput Computing. It aims to contribute significantly to the scientific community by solving pressing problems and implementing robust solutions in collaboration with the GlideinWMS and HEPCloud teams. The research sets the stage for a more efficient, scalable, and cost-effective paradigm in scientific computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

High Performance Computing Based Parallel HIearchical Modal Association Clustering (HPAR HMAC)

For many applications, clustering is a crucial step in order to gain insight into the makeup of a dataset. The best approach to a given problem often depends on a variety of factors, such as the size of the dataset, time restrictions, and soft clustering requirements. The HMAC algorithm seeks to combine the strengths of 2 particular clustering approaches: model-based and linkage-based clustering. One particular weakness of HMAC is its computational complexity. HMAC is not practical for mega-scale data clustering. For high-definition imagery, a user would have to wait months or years for a result; for a 16-megapixel image, the estimated runtime skyrockets to over a decade! To improve the execution time of HMAC, it is reasonable to consider an multi-core implementation that utilizes available system resources. An existing imple-mentation (Ray and Cheng 2014) divides the dataset into N partitions - one for each thread prior to executing the HMAC algorithm. This implementation benefits from 2 types of optimization: parallelization and divide-and-conquer. By running each partition in parallel, the program is able to accelerate computation by utilizing more system resources. Although the parallel implementation provides considerable improvement over the serial HMAC, it still suffers from poor computational complexity, O(N2). Once the maximum number of cores on a system is exhausted, the program exhibits slower behavior. We now consider a modification to HMAC that involves a recursive partitioning scheme. Our modification aims to exploit divide-and-conquer benefits seen by the parallel HMAC implementation. At each level in the recursion tree, partitions are divided into 2 sub-partitions until a threshold size is reached. When the partition can no longer be divided without falling below threshold size, the base HMAC algorithm is applied. This results in a significant speedup over the parallel HMAC.

Patlolla, Dilip R↗
DOE OSTI2018

Chemical Modification Mechanisms in Hybrid Hafnium Oxo-methacrylate Nanocluster Photoresists for Extreme Ultraviolet Patterning

The potential implementation of extreme ultraviolet (EUV) lithography into next generation device processing is bringing urgency to identify resist materials that optimize EUV lithographic performance. Inorganic/organic hybrid nanoparticles or clusters constitute a promising new class of materials, with high EUV sensitivity from the core and tunable chemistry through the coordinating ligands. Development of a thorough mechanistic understanding of the solubility switching reactions in these materials is an essential first step toward their implementation in patterning applications but remains challenging due to the complexity of their structures, limitations in EUV sources, and lack of rigorous in situ characterization. Here, we report a mechanistic investigation of the solubility switching reactions in hybrid clusters comprising a small HfOx core capped with a methacrylic acid ligand shell (HfMAA). We show that EUV-induced reactions can be studied by performing in situ infrared (IR) spectroscopy of electron-irradiated films using a variable energy electron gun. Combining additional ex situ metrology, we track the chemical evolution of the material at each stage of a typical resist processing sequence. For instance, we find that a cross-linking reaction initiated by decarboxylation of the methacrylate ligands under electron irradiation constitutes the main solubility switching mechanism, although there are also chemical changes imparted by a typical post application bake (PAB) step alone. Lastly, synchrotron-based IR microspectroscopy measurements of EUV-irradiated HfMAA films enable a comparison of reactions induced by EUV vs electron beam irradiation of the same resist material, yielding important insight into the use of electron beam irradiation as an experimental model for EUV exposure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs

Cancer is a leading cause of death in the US, second only to heart disease. It is primarily a result of a combination of an estimated two-nine genetic mutations (multi-hit combinations). Although a body of research has identified hundreds of cancer-causing genetic mutations, we don’t know the specific combination of mutations responsible for specific instances of cancer for most cancer types. An approximate algorithm for solving the weighted set cover problem was previously adapted to identify combinations of genes with mutations that may be responsible for individual instances of cancer. However, the algorithm’s computational requirement scales exponentially with the number of genes, making it impractical for identifying more than three-hit combinations, even after the algorithm was parallelized and scaled up to a V100 GPU. Since most cancers have been estimated to require more than three hits, we scaled out the algorithm to identify combinations of four or more hits using 1000 nodes (6000 V100 GPUs with ≈48×106 processing cores) on the Summit supercomputer at Oak Ridge National Laboratory. Efficiently scaling out the algorithm required a series of algorithmic innovations and optimizations for balancing an exponentially divergent workload across processors and for minimizing memory latency and inter-node communication. We achieved an average strong scaling efficiency of 90.14% (80.96%–97.96% for 200 to 1000 nodes), compared to a 100 node run, with 84.18% scaling efficiency for 1000 nodes. With experimental validation, the multi-hit combinations identified here could provide further insight into the etiology of different cancer subtypes and provide a rational basis for targeted combination therapy.

Dash, Sajal↗
DOE OSTI2015

Efficient Parallel Discrete Event Simulation on Cloud/Virtual Machine Platforms

Cloud and Virtual machine (VM) technologies present new challenges with respect to performance and monetary cost in executing parallel discrete event simulation (PDES) applications. Due to the introduction of overall cost as a metric, the traditional use of the highest-end computing configuration is no longer the most obvious choice. Moreover, the unique runtime dynamics and configuration choices of Cloud and VM platforms introduce new design considerations and runtime characteristics specific to PDES over Cloud/VMs. Here, an empirical study is presented to guide an understanding of the dynamics, trends, and trade-offs in executing PDES on Cloud/VM platforms. Performance and cost measures obtained from multiple PDES applications executed on the Amazon EC2 Cloud and on a high-end VM host machine reveal new, counterintuitive VM--PDES dynamics and guidelines. One of the critical aspects uncovered is the fundamental mismatch in hypervisor scheduler policies designed for general cloud workloads versus the virtual time ordering needed for PDES workloads. This insight is supported by experimental data revealing the gross deterioration in PDES performance traceable to VM scheduling policy. To overcome this fundamental problem, the design and implementation of a new deadlock-free scheduler algorithm are presented, optimized specifically for PDES applications on VMs. The scalability of our scheduler has been tested up to 128 VMs multiplexed on 32 cores, showing significant improvement in the runtime relative to the default Cloud/VM scheduler. The observations, algorithmic design, and results are timely for emerging cloud/VM-based installations, highlighting the need for PDES-specific support in high performance discrete event simulations on Cloud/VM platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Thermal profile shaping and loss impacts of strain annealing on magnetic ribbon cores

We report the use of the advanced manufacturing technique of strain annealing for nanocomposite magnetic ribbons enables control of relative permeabilities and spatially dependent permeability profiles. Tuned permeability profiles enable enhanced control of the magnetic flux throughout magnetic cores, including the concentration or dispersion of the magnetic flux over specific regions. Due to the correlation between local core losses and temperature rises with the local magnetic flux, these profiles can be tuned at the component level for improved losses and reduced steady-state temperatures. We present analytical models for a number of assumed permeability profiles. This work shows significant reductions in the peak temperature rise with overall core losses impacted to a lesser extent. Controlled strain annealing profiles can also adjust the location of hotspots within a component for optimal cooling schemes. Finally, as a result, magnetic designs can have improved performance for a range of potential operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

KIVA Software

KIVA is a family of Fortran-based Computational Fluid Dynamics (CFD) software that predicts complex multi-species multi-phase turbulent fuel and air flows. KIVA models all engine combustion processes with models for spray, soot formation, spark ignition, and reactive chemistry (including pollutantformation processes). Employing KIVA software helps to optimize internal combustion engine processes, including diesel engines, for higher efficiency and lower emissions. KIVA engine modeling routines such as spray and core CFD Arbitrary Lagrangian-Eulerian (ALE) algorithms are now ubiquitous in the industry of engine code simulators. In addition, many engineers and researchers have made modifications to or enhanced our routines to address specific problems in engine designs.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

Thermomechanical Modeling of Hydride Materials for Tritium Storage Beds (FY18 Report)

Thermomechanical Modeling of Hydride Materials for Tritium Storage Beds This project aims to develop a fundamental modeling capability suitable for the simulation and assessment of various hydride materials and their associated storage vessels and deuterium/tritium delivery methods. If successful, this work will: • Enhance a core competency of SRNL by adding a unique capability to the tritium processing R&D efforts currently performed for NNSA Defense Programs • Optimize the design and operation of future hydrogen storage technologies in anticipation of increased demands and the call for a robust, reliable, resilient, and responsive stockpile • Establish SRNL as a valuable analytic resource for DOE and DoD decision makers and the foreign technology assessment community, which has already shown significant interest in developing such models to assess the likelihood and feasibility of alternative foreign gas (deuterium/tritium) storage and delivery systems that might provide new technical pathways for the U.S. and to determine the performance of alternative foreign systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ReNew100: Reliable Power System Operation with 100% Renewable Generation

The ReNew100 project has developed and demonstrated an operator support system (OSS) to operate power systems with 100% renewable power generation from inverter-based resources (IBRs) like wind and solar that significantly reduces the risk of widespread power outages in a simulated operational environment at Technology Readiness Level (TRL) 6. The OSS achieves resilient operation for power systems under changing operation modes including varying combinations of conventional and renewable generation, including cases with 100% renewable generation from wind and solar IBRs. The OSS continuously monitors the N-1 security of the power system, which refers to the ability of the power systems to survive single outages of power system elements, like the loss of a generator or a power line, without causing widespread power outages beyond those expected and planned for. If the OSS identifies that the system is not N-1 secure, an automatic Controller Parameter Optimization (CoPO) developed within Renew100 is activated to optimize the N-1 security of the system by tuning controller parameters within different assets like batteries, wind, and solar plants. The core innovation of ReNew100 is the development of this CoPO as well as the demonstration of the OSS for a real-time simulation of the Hawai'i Island power system. Moreover, Renew100 has developed new model calibration techniques for dynamic power system models and demonstrated them for Hawai'i Island.

24 POWER TRANSMISSION AND DISTRIBUTION↗