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Study of Energy Saving Analysis for Different Industries
This study analyzes the energy consumption and saving performance in the industries in the U.S.A. All energy assessments implemented were for facilities whose annual energy consumptions were less than 9,000,000 kWh (small- and medium-sized industries) that belong to the manufacturing industries with Standard Industrial Classification (SIC) codes ranging from 2000 to 3999 in addition to SIC codes starting with 49. In this study, assessments are classified based on the SIC codes with recommendations analysis for each classification to get a better idea of what recommendations were suggested in each major industrial sector, knowing that 68 assessments were made, and their SIC ranged from 14 to 49. In addition, this study could be considered as a guide for energy engineers and other personnel involved in the energy assessment process. The information investigated can give a better prediction for composing better energy-demanding industries and minimizing energy consumption. More than 61 energy assessments were conducted for manufacturing facilities and analyzing the data gathered and processed. Through the research, the Fabricated Metal industry achieved the highest average kWh savings and cost savings within the industries studied in this study. According to the average gigajoule (GJ) savings, the fabricated metal industry ranked second within the studied industries. Conversely, Food and Kindred Products achieved the highest GJ energy savings within the studied industries. Overall, lighting, motors, compressors, and heating, ventilation, and air conditioning (HVAC) were the most contributing industries in a total of 547 recommendations.
Minimizing specific energy consumption using digital twins
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Minimizing specific energy consumption using digital twins
Explore the source record for details and available documents.
Energy, Exergy, Entropy Generation Minimization, and Exergoenvironmental Analyses of Energy Systems-A Mini-Review
Meeting the growing energy demands has become a crucial challenge, which should employ energy integration and benefit from the extraction of the maximum thermodynamic potential of the resources consumed. This brief review presents an overview of the essential elements of the methods of energy, exergy, entropy generation minimization, and exergoenvironmental analyses. In combination, these methodologies constitute a powerful toolbox for the design, analysis, and optimization of energy systems. The quantification of energy and environmental impacts provided by them is essential in guiding toward system designs that are consistent with current energy and environmental needs.
Surrogate Hessian accelerated structural optimization for stochastic electronic structure theories
In this work, we present an efficient energy-based method for structural optimization with stochastic electronic structure theories, such as diffusion quantum Monte Carlo (DMC). This method is based on robust line-search energy minimization in reduced parameter space, exploiting approximate but accurate Hessian information from a surrogate theory, such as density functional theory. The surrogate theory is also used to characterize the potential energy surface, allowing for simple but reliable ways to maximize statistical efficiency while retaining controllable accuracy. We demonstrate the method by finding the minimum DMC energy structures of the selected flake-like aromatic molecules, such as benzene, coronene, and ovalene, represented by 2, 6, and 19 structural parameters, respectively. In each case, the energy minimum is found within two parallel line-search iterations. The method is near-optimal for a line-search technique and suitable for a broad range of applications. It is easily generalized to any electronic structure method where forces and stresses are still under active development and implementation, such as diffusion Monte Carlo, auxiliary-field Monte Carlo, and stochastic configuration interaction, as well as deterministic approaches such as the random-phase approximation. Accurate and efficient means of geometry optimization could shed light on a broad class of materials and molecules, showing high sensitivity of induced properties to structural variables.
Nitrogen-Fixing Gamma Proteobacteria Azotobacter vinelandii—A Blueprint for Nitrogen-Fixing Plants?
The availability of fixed nitrogen limits overall agricultural crop production worldwide. The so-called modern “green revolution” catalyzed by the widespread application of nitrogenous fertilizer has propelled global population growth. It has led to imbalances in global biogeochemical nitrogen cycling, resulting in a “nitrogen problem” that is growing at a similar trajectory to the “carbon problem”. As a result of the increasing imbalances in nitrogen cycling and additional environmental problems such as soil acidification, there is renewed and increasing interest in increasing the contributions of biological nitrogen fixation to reduce the inputs of nitrogenous fertilizers in agriculture. Interestingly, biological nitrogen fixation, or life’s ability to convert atmospheric dinitrogen to ammonia, is restricted to microbial life and not associated with any known eukaryotes. It is not clear why plants never evolved the ability to fix nitrogen and rather form associations with nitrogen-fixing microorganisms. Perhaps it is because of the large energy demand of the process, the oxygen sensitivity of the enzymatic apparatus, or simply failure to encounter the appropriate selective pressure. Whatever the reason, it is clear that this ability of crop plants, especially cereals, would transform modern agriculture once again. Successfully engineering plants will require creating an oxygen-free niche that can supply ample energy in a tightly regulated manner to minimize energy waste and ensure the ammonia produced is assimilated. Nitrogen-fixing aerobic bacteria can perhaps provide a blueprint for engineering nitrogen-fixing plants. This short review discusses the key features of robust nitrogen fixation in the model nitrogen-fixing aerobe, gamma proteobacteria Azotobacter vinelandii, in the context of the basic requirements for engineering nitrogen-fixing plants.
Hydrogen irradiation-driven computational surface chemistry of lithium oxide and hydroxide
We have investigated, using molecular dynamics, the surface chemistry of hydrogen incident on the amorphous and crystalline lithium oxide and lithium hydroxide surfaces upon being slowed down by a collision cascade and retained in the amorphous surface of either Li 2 O or LiOH. We looked for the bonding of H to the resident structures in the surface to understand a possible chain of chemical reactions that can lead to surface transformation upon H atom impact. Our findings, using Density-Functional Theory (DFT) trained ReaxFF force field/electronegativity equalization method potentials, stress the importance of inclusion of polarization in the dynamics of a Li–O–H system, which is also illustrated by DFT energy minimization and quantum–classical molecular dynamics using tight binding DFT. The resulting polar-covalent chemistry of the studied systems is complex and very sensitive to the instantaneous positions of all atoms as well as the ratio of concentrations of various resident atoms in the surface.
Constrained Local Approximate Ideal Restriction for Advection-Diffusion Problems
Herein this paper focuses on developing a reduction-based algebraic multigrid (AMG) method that is suitable for solving general (non)symmetric linear systems and is naturally robust from pure advection to pure diffusion. Initial motivation comes from a new reduction-based AMG approach, $\ell \text{AIR}$ (local approximate ideal restriction), that was developed for solving advection-dominated problems. Though this new solver is very effective in the advection-dominated regime, its performance degrades in cases where diffusion becomes dominant. This is consistent with the fact that in general, reduction-based AMG methods tend to suffer from growth in complexity and/or convergence rates as the problem size is increased, especially for diffusion-dominated problems in two or three dimensions. Motivated by the success of $\ell \text{AIR}$ in the advective regime, our aim in this paper is to generalize the AIR framework with the goal of improving the performance of the solver in diffusion-dominated regimes. To do so, we propose a novel way to combine mode constraints as used commonly in energy-minimization AMG methods with the local approximation of ideal operators used in $\ell \text{AIR}$. The resulting constrained $\ell \text{AIR}$ algorithm is able to achieve fast scalable convergence on advective and diffusive problems. In addition, it is able to achieve standard low complexity hierarchies in the diffusive regime through aggressive coarsening, something that was previously difficult for reduction-based methods.
Equilipy: a python package for calculating phase equilibria
The CALPHAD (CALculation of PHAse Diagram) approach (Nigel Saunders & Miodownik, 1998) provides predictions for thermodynamically stable phases in multicomponent-multiphase materials across a wide range of temperatures. Consequently, the CALPHAD calculations became an essential tool in materials and process design (Luo, 2015). Such design tasks frequently require navigating a high-dimensional space due to multiple components involved in the system. This increasing complexity demands high-throughput CALPHAD calculations, especially in the rapidly evolving field of alloy design. In response to the need, we developed Equilipy an open-source Python package designed for calculating phase equilibria of multicomponent-multiphase systems. Equilipy is specifically tailored for high-throughput CALPHAD calculations, offering parallel computations across multiple processors and nodes with the given NPT input conditions namely elemental compositions (N), pressure (P), and temperature (T). Equilipy utilizes the program structure and Gibbs energy functions from the Fortran-based program, Thermochimica (Piro et al., 2013), with incorporating a new Gibbs energy minimization algorithm. This algorithm, originally developed by Capitani and Brown in 1987 (Capitani & Brown, 1987), has been revised and implemented to enhance the stability and performance of calculations. The Fortran codes are precompiled and interfaced with Python via F2PY, ensuring high computation speed. Benchmark tests shown in Figure 1 demonstrate that Equilipy’s computation speed is comparable to those of established commercial software, TC-Python and PanPython. This result highlights its efficiency and potential applications in various scientific and industrial fields.
An interplay between a hydrogen atmosphere and dislocation characteristics in BCC Fe from time-averaged molecular dynamics
The interplay between hydrogen and dislocations (e.g., core and elastic energies, and dislocation–dislocation interactions) has implications on hydrogen embrittlement but is poorly understood. Continuum models of hydrogen enhanced local plasticity have not considered the effect of hydrogen on dislocation core energies. Energy minimization atomistic simulations can only resolve dislocation core energies in hydrogen-free systems because hydrogen motion is omitted so hydrogen atmosphere formation can’t occur. Additionally, previous studies focused more on face-centered-cubic than body-centered-cubic metals. Discrete dislocation dynamics studies of hydrogen–dislocation interactions assume isotropic elasticity, but the validity of this assumption isn’t understood. Here, we perform time-averaged molecular dynamics simulations to study the effect of hydrogen on dislocation energies in body-centered-cubic iron for several dislocation character angles. We see atmosphere formation and highly converged dislocation energies. We find that hydrogen reduces dislocation core energies but can increase or decrease elastic energies of isolated dislocations and dislocation–dislocation interaction energies depending on character angle. We also find that isotropic elasticity can be well fitted to dislocation energies obtained from simulations if the isotropic elastic constants are not constrained to their anisotropic counterparts. These results are relevant to ongoing efforts in understanding hydrogen embrittlement and provide a foundation for future work in this field.
Symmetry breaking and self-interaction correction in the chromium atom and dimer
Density functional approximations to the exchange–correlation energy can often identify strongly correlated systems and estimate their energetics through energy-minimizing symmetry-breaking. In particular, the binding energy curve of the strongly correlated chromium dimer is described qualitatively by the local spin density approximation (LSDA) and almost quantitatively by the Perdew–Burke–Ernzerhof generalized gradient approximation (PBE-GGA), where the symmetry breaking is antiferromagnetic for both. Here, we show that a full Perdew–Zunger self-interaction-correction (SIC) to LSDA seems to go too far by creating an unphysical symmetry-broken state, with effectively zero magnetic moment but non-zero spin density on each atom, which lies ~4 eV below the antiferromagnetic solution. A similar symmetry-breaking, observed in the atom, better corresponds to the 3d↑↑4s↑3d↓↓4s↓ configuration than to the standard 3d↑↑↑↑↑4s↑. For this new solution, the total energy of the dimer at its observed bond length is higher than that of the separated atoms. In conclusion, these results can be regarded as qualitative evidence that the SIC needs to be scaled down in many-electron regions.
Relaxation of the turbulent magnetosheath
In turbulence, non-linear terms drive energy transfer from large-scale eddies into small scales through the so-called energy cascade. Turbulence often relaxes toward states that minimize energy; typically these states are considered globally. However, turbulence can also relax toward local quasi-equilibrium states, creating patches or cells where the magnitude of non-linearity is reduced and the energy cascade is impaired. We show, using data from the Magnetospheric Multiscale (MMS) mission, and for the first time, compelling observational evidence that this ‘cellularization’ of turbulence can occur due to local relaxation in a strongly turbulent natural environment such as the Earth’s magnetosheath.
Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning
Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.
Optimizing Phase Change Composite Thermal Energy Storage Using the Thermal Ragone Framework
The thermal Ragone framework can be applied to the design of thermal energy storage heat exchangers to identifying relationships between their power requirements and energy capabilities. This presentation focuses on the design of planar thermal energy storage heat exchangers with phase change materials and thermal conductivity additives. Finite-difference models were used to optimize thermal storage heat exchanger designs focusing on key design parameters of conductivity additive volume fraction, spacing between tubes, and the phase transition temperatures. Design trends were developed for optimal designs based on varying thermal power requirements. To aid in future device design, models of reduced complexity were developed with the ability to predict optimal designs with computational speeds up to 5,000 times faster than finite-difference models.
Analysis of Conduction Cooling Strategies for Wire Arc Additive Manufacturing
Metal additive manufacturing (AM) processing consists of numerous parameters which take time to optimize for various geometries. One aspect of the metal AM process that continues to be explored is the control of thermal energy accumulation during component manufacturing due to the melting and solidification of the feedstock. Excessive energy accumulation causes thermal failure of the component while minimal energy accumulation causes lack of fusion with the build plate or previous layer. The ability to simulate the thermal response of an AM component can increase research efficiency by reducing the time to optimize thermal energy accumulation. This paper presents an effective implementation of finite element analysis to determine the thermal response of a wire arc additive manufactured component with various build plate sizes and cooling methods including, integral build plate cooling, oversized build plates with passive cooling, and non-integral build plate cooling. The use of integral build plate cooling channels was shown to decrease the interpass temperature at the conclusion of the build process by 55% and build plate temperature by 96% compared to the conventionally deposited sample with 20 second dwell time. The use of a tall build plate with passive cooling was shown to reduce the interpass temperature by 32% as compared to the conventionally deposited sample with 20 second dwell time. Each cooling strategy evaluated decreased the interpass temperature within a range of 20–55% which enables higher deposition rates and decreased dwell times during depositions. The cooling strategies are designed to be implemented in a hybrid or retrofit AM platform to mitigate concerns of the thermal input from the additive process having detrimental effects on the precision of the machining process. This paper shows that accurate simulations of all strategies can be used to accurately predict the thermal response of the various strategies discussed. These cooling strategies will allow for increased deposition rates with comparable interpass temperature and decreased dwell time, increasing deposition efficiency. This model and these simulations are verified by experimental results. It is concluded that passive strategies, such as the over-sized tall build plate, can be used when liquid coolant in the AM environment could negatively affect the deposition process. Active cooling strategies, such as the integral build plate cooling could be used if low thermal conductivity materials are deposited or higher material deposition rates are desired. This paper discusses the use of active and passive cooling used during AM and shows how a simulation model can be used to make design choices for cooling strategies. The model also enables verification of select critical process parameters such as dwell times for a desired interpass temperature.
Characteristics of Ultralow Aspect Ratio Toroidal Plasmas and Non-Solenoidal Startup and Edge Stability Studies at Near-Unity Aspect Ratio (Final Report)
This grant supported a long-term research and development program to explore the physics and technology of spherical tokamak (ST) plasma behavior at near-unity aspect ratio ( A ). The PEGASUS Toroidal Experiment is a university-scale ( R ) ~ 0.40 m, α ~ .35 m, I p ≤ 0.3 MA, B T ≤ 0.15 T) experiment that accesses A → 1, to provide large values of the normalized plasma current, I N = I p /α B T , or equivalently the toroidal field utilization factor I p / I TF . The research activities focused on: 1) developing the technique of Local Helicity Injection (LHI) as a viable means of initiating and growing an ST plasma without the use of a central solenoid; 2) exploring the equilibrium and stability properties of spherical tokamak plasmas at extremely low aspect ratio; and 3) obtaining new insights in the properties of the enhanced confinement H-mode regime at A ~ 1. The initial (Phase I) facility employed a unique high-stress central solenoid Ohmic for current drive and heating with simple magnet current waveforms. A subsequent major upgrade (Phase II) offered more flexible control of the plasma evolution along with a powerful LHI system for plasma initiation, heating and sustainment. The LHI technique consists of injection of high-density current streams into vacuum toroidal and vertical magnetic fields. These streams become unstable and support magnetic reconnection to allow the system to relax into a tokamak-like magnetic configuration. This program focused on: 1) development of electron current sources that provide sufficiently high injection currents in the challenging edge plasma region; 2) validation of theoretical limits to achievable I p arising from magnetic helicity and energy conservation plus absolute limits arising from relaxation to a minimal energy states; 3) tests of a power balance model to describe the evolution of the plasma current I p (t); and 4) study of magnetic fluctuations and their role in the I p evolution. This culminated in the production of toroidal currents of I p ≥ 0.2 MA with only ~8 kA of injected current. Operation with Ohmic induction alone in Phase I created plasmas with soft limits on the achievable I p and field utilization ( I p / I TF ~ 1) due to the onset of large low-order (2/1 or 3/2) internal resonant tearing modes. With the expanded capabilities in Phase II operation, methods employed to mitigate those modes and achieve I p / I TF > 1 were: 1) I TF ramp-downs; 2) non-solenoidal startup via LHI; and 3) extremely low I TF Ohmic operations assisted by LHI current injector pre-ionization. Extensive use of LHI-driven startup and drive for non-solenoidal operation produced favorable current profiles to stably operate tokamak plasmas with unprecedentedly high I N = I p / aB T ~ 15 and I p / I TF > 2. With strong reconnection-driven anomalous ion heating, this in turn provided access to tokamak plasmas with B t up to 100%, a world record 2 times higher than that previously achieved in advanced or spherical tokamaks. This high B t regime had a large fraction (~50%) of the plasma volume in a region of an absolute minimum- B configuration. The MHD stability limit in this regime was the ideal external kink mode without a stabilizing wall. These results provided the first demonstration of access to tokamak plasmas with very high B t , high i N , high elongation κ, and low internal inductance ℓ i , and as such served as a proof-ofprinciple for access to this long-sought ST regime. The naturally low B T needed for stability at A ≤ 1.2 reduces the L-H mode power threshold, P LH , so that H-mode plasmas were achieved with Ohmic heating alone. The power threshold for the transition to the H-mode regime was found to be 10× the accepted values from the ITER international scaling, confirming initial trends indicated by experiments on MAST and NSTX, and suggesting that the physics behind this transition is not yet fully understood. The density dependence of the transition and the observed insensitivity to whether the plasma is bounded by a limiter or magnetic divertor are consistent with the recent FM 3 model. Multiple unstable toroidal magnetic modes were measured during ELM (Edge Localized Mode) crashes, with generally lower toroidal mode numbers than seen at higher A , which was attributed to the larger peeling mode instability drive at low A . First-ever edge current profile measurements through an ELM crash showed current-carrying filaments were generated, as seen in ELM simulations and in electromagnetic blob transport theory.
VOLTTRON/volttron-pnnl-aems
The Autonomous Energy Management Software (AEMS) system will continuously optimize the operations of the distributed energy resources in the small and medium size commercial building by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. Initially, AEMS system will manage rooftop air conditioners and heat pumps but it can be extended in the future to manage, hot water heaters, storage (battery and thermal), electric vehicle charging and monitoring solar photovoltaic. AEMS support both energy efficiency and grid service features.