Physico-Mathematical Justification of the Feasibility of the Autonomous Energy Generator VENDOR: Rigorous Validation Based on Spaceborne Observations of Electrostatic Solitons
Authors: O.Krishevich, V.Peretyachenko
Abstract
This paper presents a rigorous physico-mathematical foundation supporting the feasibility of the autonomous energy generator VENDOR (patent WO2024209235). The methodology is grounded in recently published space-based studies of electrostatic solitary waves (ESWs / ES structures) in the Earth’s magnetosphere (Leonenko et al., JETP Letters, 2025), and comprises the following key stages:
- Mathematical modeling of avalanche ionization in gaseous or rarefied media based on the Townsend mechanism, incorporating space charge effects and the Raether limit constraint.
- Derivation of resonance phenomena and parametric amplification, including nonlinear components, mode coupling, and saturation-resilience analysis.
- Analysis of multimodule synchronization, involving phase locking of oscillatory modes, field superposition effects, and dynamic phase-shift compensation.
- Rigorous thermodynamic verification, including energy balance, continuity, conservation laws (energy and entropy), and comprehensive evaluation of loss channels (thermal, radiative, recombinative, etc.).
Within the proposed model, it is demonstrated that under specific configurations — including gas/plasma density, electrode geometry, field topology, and coherent phase alignment — a net positive energy amplification coefficient can be achieved.
1. Introduction
Contemporary science is faced with a foundational question:
Is it possible to engineer autonomous energy sources with a conversion coefficient (net energy gain) exceeding unity, without violating the fundamental laws of physics—particularly the laws of thermodynamics and energy conservation?
A key requirement in this context is rigorous control over all energy-exchange processes, including loss mechanisms, nonlinear feedback, saturation effects, and fluctuations.
In recent years, the Magnetospheric Multiscale Mission (MMS) has provided high-resolution data on electromagnetic and electrostatic disturbances within the Earth’s magnetosphere (e.g., Hansel et al., Mapping MMS Observations of Solitary Waves, 2021). Notably, Leonenko et al. (2025) reported intense electrostatic solitary waves (ESWs) in the Central Plasma Sheet (CPS) of the magnetotail, with electric field amplitudes reaching ~100 mV/m.
These structures are stable nonlinear waveforms capable of transporting and redistributing energy within plasma environments with minimal dissipative losses.
Such naturally occurring phenomena open an intriguing possibility:
If the physical mechanisms underpinning ESWs can be adapted for use in engineered systems, they may enable novel modes of energy transformation—approaching net amplification—while remaining within the bounds of classical physics.
However, significant differences exist between spaceborne plasma environments and terrestrial devices (e.g., density, scale, boundary conditions, inhomogeneity, dissipative losses, and instabilities). This necessitates a rigorous physico-mathematical translation and validation of the underlying principles.
This work presents a stepwise, internally coherent justification for the feasibility of the VENDOR autonomous energy generator, structured as follows:
- Mathematical modeling of avalanche and corona ionization in gas/plasma media, accounting for space charge buildup and the Raether limit.
- Analysis of resonance phenomena, parametric amplification, nonlinear interactions, and saturation dynamics.
- Multimodal phase synchronization within a modular system architecture, including field alignment and active phase compensation.
- Thermodynamic validation, covering complete energy balance, dissipation mechanisms, system stability, and compliance with conservation laws.
We demonstrate that, under carefully tuned physical parameters (geometry, medium density, field intensities), it is possible to achieve a net energy amplification coefficient
\begin{equation}
K_{\rm total} > 1 \tag{1}
\end{equation}
without any violation of fundamental physical laws.
The following sections provide theoretical derivations, numerical evaluations, and experimental observations consistent with the proposed model.
2. Theoretical Foundations
2.1 Parameters of Electrostatic Solitons and Technical Analog
In the study by Leonenko et al. (2025) — along with related investigations into electrostatic structures in the Earth’s magnetosphere — the following average and peak parameters of electrostatic solitary waves (ESWs) in the central plasma sheet (CPS) of the magnetotail have been documented. For clarity and precision, we report the values with their stated uncertainties:
Temporal Characteristics
Duration of a single solitonic pulse:
\begin{equation}
\tau = (15 \pm 5)\times 10^{-3}\ \mathrm{s} \tag{2}
\end{equation}
Interaction / coherence time (i.e., the duration over which the structure remains spatially localized):
\begin{equation}
\Delta t = (12 \pm 3)\times 10^{-3}\ \mathrm{s} \tag{3}
\end{equation}
Electrical Characteristics
Average electric field amplitude:
E = (25 \pm 8)\times 10^{-3}\ \mathrm{V/m}, \quad \text{with peaks up to } 100\times10^{-3}\ \mathrm{V/m} \tag{4}
\end{equation}
Longitudinal propagation velocity of the soliton:
\begin{equation}
v = (650 \pm 350)\ \mathrm{km/s} \tag{5}
\end{equation}
Beam Energetic Parameters
Change in kinetic energy per electron:
\Delta E_{\rm beam} = (1.0 \pm 0.1)\ \mathrm{keV} = (1.602 \pm 0.016)\times10^{-16}\ \mathrm{J} \tag{6}
\end{equation}
Electron beam density (possibly off-peak):
n_{\rm beam} = (0.15 \pm 0.02)\ \mathrm{cm}^{-3} = (1.5 \pm 0.2)\times10^{5}\ \mathrm{m}^{-3} \tag{7}
\end{equation}
Observed power density:
P_{\rm obs} = j \cdot E’ \approx (0.5 \pm 0.3)\ \mathrm{nW/m^3} \quad \text{(mean)}, \quad \text{with peaks up to } (2.5 \pm 0.5)\ \mathrm{nW/m^3} \tag{8}
\end{equation}
These measurements indicate that ESWs function as localized, stable nonlinear structures with sustained electric fields, capable of transporting energy across the plasma with minimal dissipative losses.
In the literature, theoretical frameworks describing ESWs often invoke BGK modes and phase space holes, as well as ion-acoustic and electron-acoustic solitons, to model such multi-component plasma dynamics.
Technical Analogy for the VENDOR Generator
For the engineered realization of the VENDOR generator, we propose a technical analogy:
to replicate the localized field structure and charge density distribution of an ESW at a reduced scale, within a controlled environment (e.g., a low-density gas or weakly ionized plasma), such that a stable soliton-like regime with comparable amplitude and temporal persistence may be sustained.
Key engineering challenges in this approach include:
- Downscaling and confinement of plasma density
- Collision frequency control and energy relaxation management
- Stabilization of fluctuations in confined geometry
- Compensation for thermal and radiative losses
By addressing these factors, it becomes feasible to design a laboratory-scale system that emulates the core energetic features of spaceborne electrostatic solitons, thereby laying the foundation for novel energy conversion mechanisms.
2.2 Physical Model of Processes in the VENDOR Generator
2.2.1 Avalanche Ionization (Townsend Model)
We consider the generation of free charge carriers (electrons and ions) in the working medium (gas or weakly ionized plasma) via avalanche ionization, described by the Townsend mechanism. The fundamental balance equation for electron concentration is:
\frac{\partial n_e}{\partial t} = \alpha(E)\,n_e\,v_d – \beta\,n_e^2 + \gamma_{\rm photo}\,I_{\rm UV} + S_{\rm ext} \tag{9}
\end{equation}
where:
- $n_e(x,t)$ — electron concentration [m⁻³]
- $\alpha(E)$ — ionization coefficient, field-dependent [m⁻¹]
- $v_d = \mu_e\,E$ — electron drift velocity under electric field $E$ [m/s]
- $\beta$ — electron–ion recombination coefficient [m³/s]
- $\gamma_{\rm photo}$ — photoionization coefficient [m²·s⁻¹·W⁻¹]
- $I_{\rm UV}$ — intensity of external UV radiation [W/m²]
- $S_{\rm ext}$ — external ionization sources (e.g., radiation, particle injection) [m⁻³·s⁻¹]
For gaseous environments under standard or modified pressure, the Townsend approximation is often applied:
\begin{equation}
\alpha(E) = A\,p\,\exp\!\left(-\frac{B\,p}{E}\right) \tag{10}
\end{equation}
where $A$ and $B$ are empirical constants, and $p$ is the gas pressure.
In this example, the constants used were:
A = 15\,\mathrm{m^{-1}\cdot torr^{-1}}, \quad B = 365\,\mathrm{V\,m^{-1}\cdot torr^{-1}} \tag{11}
\end{equation}
which are typical for air under specific conditions, and should be verified for applicability to the working gas mixture in the VENDOR setup.
For a given configuration (electrode gap $d$, electric field $E$, and pressure $p$), the critical condition for avalanche breakdown is expressed as:
\alpha(E)\,d \ge \ln\left(1 + \frac{1}{\gamma_e}\right) + \Delta_{\rm enhancement} \tag{12}
\end{equation}
where:
- $d$ — electrode gap [m]
- $\gamma_e$ — secondary electron emission coefficient (dimensionless)
- $\Delta_{\rm enhancement}$ — correction factor accounting for collective effects (multi-particle interactions, spatial fluctuations, nonlinear mutual ionization)
Numerical Example:
For $d = 2 \times 10^{-2}\ \mathrm{m}$, $E = 10^6\ \mathrm{V/m}$, $p = 760\ \mathrm{torr}$:
\alpha(E) = 15 \cdot 760 \exp\!\left(-\frac{365 \cdot 760}{10^6}\right) \approx 11,400 \cdot \exp(-0.277) \approx 8,745\ \mathrm{m^{-1}} \tag{13}
\end{equation}
Then:
\begin{equation}
\alpha(E)\,d = 8,745 \cdot 0.02 = 175 \tag{14}
\end{equation}
Assuming $\gamma_e = 0.1$, and $\Delta_{\rm enhancement} \approx 1$, the right-hand side of Eq. (12) becomes:
\begin{equation}
\ln(1 + 10) + 1 \approx \ln(11) + 1 \approx 2.4 + 1 = 3.4 \tag{15}
\end{equation}
Thus, $\alpha d \gg 3.4$, seemingly satisfying the breakdown condition.
However, this estimation assumes:
- A static uniform medium without accounting for space-charge effects, field distortion, current limitations, or feedback loops.
- The plasma growth rate, current distribution, and dissipative mechanisms (recombination, diffusion, charge leakage) must be evaluated to determine practical feasibility.
- Importantly, this criterion must be linked to the onset of soliton-like field structures, not merely uncontrolled avalanche discharge.
2.2.2 Poisson Equation and Potential Distribution
The electrostatic potential $\phi(x,t)$ is governed classically by the Poisson equation:
\begin{equation}
\nabla^2 \phi = – \frac{\rho(x,t)}{\varepsilon_0} \tag{16}
\end{equation}
where the charge density is:
\begin{equation}
\rho(x,t) = e\,\bigl(n_i – n_e + n_+ – n_- \bigr) \tag{17}
\end{equation}
In a 1D approximation along the x-axis (as in a corona or inter-electrode discharge gap), this simplifies to:
\begin{equation}
\frac{d^2\phi}{dx^2} = -\frac{e}{\varepsilon_0} \bigl[n_i(x) – n_e(x) \bigr) \tag{18}
\end{equation}
Under the assumption of quasi-neutrality in the plasma bulk (i.e., $n_i \approx n_e$), deviations from neutrality become significant only near electrodes or in space-charge layers. In these regions, the electric field is dominated by localized charge separation.
The characteristic screening scale is the Debye length:
\begin{equation}
\lambda_D = \sqrt{\frac{\varepsilon_0 k_B T_e}{n_e e^2}} \tag{19}
\end{equation}
Example:
For $T_e = 1\ \mathrm{eV}$ (≈11,600 K), and $n_e = 10^{15}\ \mathrm{m^{-3}}$:
\lambda_D = \sqrt{\frac{8.85 \times 10^{-12} \cdot 1.38 \times 10^{-23} \cdot 11600}{10^{15} \cdot (1.602 \times 10^{-19})^2}} \approx 7.4 \times 10^{-7}\ \mathrm{m} \tag{20}
\end{equation}
Important Considerations:
- These Debye lengths are typical for dense plasmas; in rarified gases or low-ionization media, $\lambda_D$ can be much larger.
- In practical implementation, the charged region’s thickness (or field structure width) must span several $\lambda_D$ to ensure stable confinement.
- In ESW observations, spatial extents typically range from ~1 to 10 Debye lengths, supporting the analogy to localized electrostatic structures.
- Theoretical models describing stable nonlinear field configurations often rely on Schamel-type equations, modified Korteweg–de Vries (KdV) models, or BGK modes.
Thus, it is essential to self-consistently link the profiles of $n_e(x)$, $n_i(x)$, and $\phi(x)$ with the proposed solitonic field structure in the VENDOR generator.
2.2.2.1 Boundary Conditions for the Poisson Equation in the VENDOR System
To formulate a well-posed problem for the electrostatic potential distribution $\varphi(r)$, one must impose physically motivated boundary conditions, consistent with the geometry and electrode configuration of the VENDOR generator.
System Geometry and Problem Setup
- Central electrode (anode): cylinder of radius $r_1 = 1\,\mathrm{mm}$
- Outer electrode (cathode): coaxial cylindrical shell with radius $r_2 = 20\,\mathrm{mm}$
- Electrode gap: $d = r_2 – r_1 = 19\,\mathrm{mm}$
- Applied voltage: $U = 30\,\mathrm{kV}$
Assuming axial symmetry (no dependence on angular coordinate $\theta$ or axial coordinate $z$), the Poisson equation in cylindrical coordinates simplifies to:
\frac{1}{r}\,\frac{d}{dr}\!\left( r \frac{d\varphi}{dr} \right) = -\frac{\rho(r)}{\varepsilon_0} \tag{21}
\end{equation}
Dirichlet (First-Type) Boundary Conditions
At the anode $(r = r_1)$:
\begin{equation}
\varphi(r_1) = U = 30\,000\ \mathrm{V} \tag{22}
\end{equation}
The anode is assumed to be a perfect conductor, with uniform surface potential.
At the cathode $(r = r_2)$:
\begin{equation}
\varphi(r_2) = 0\ \mathrm{V} \tag{23}
\end{equation}
Neumann (Second-Type) Boundary Condition at the Anode Surface
Electron emission from the anode surface contributes a current density given by the Richardson-Dushman equation:
\begin{equation}
j_{\rm emission} = A_R\,T^2 \exp\!\left(-\frac{W}{k_B T}\right) \tag{24}
\end{equation}
with parameters:
- $A_R = 1.2 \times 10^6 \,\mathrm{A/(m^2 \cdot K^2)}$
- $T = 800\,\mathrm{K}$
- $W = 4.5\,\mathrm{eV}$
This yields:
\begin{equation}
j_{\rm emission} \approx 1.16 \times 10^9\ \mathrm{A/m^2} \tag{25}
\end{equation}
Then, at the anode:
\begin{equation}
\varepsilon_0 \left.\frac{\partial \varphi}{\partial r}\right|_{r=r_1} = -\frac{j_{\rm emission}}{v_d} \tag{26}
\end{equation}
where $v_d$ is the electron drift velocity.
Secondary Emission Boundary Condition at the Cathode
The secondary electron emission coefficient is modeled as:
\gamma_{\rm secondary} = \delta_0\left[1 – \exp\!\left(-\frac{E}{E_0}\right)\right], \quad \delta_0 = 1.2, \quad E_0 = 50\,\mathrm{eV} \tag{27}
\end{equation}
At $E \approx 1\,\mathrm{keV}$:
\begin{equation}
\gamma_{\rm secondary} \approx 1.2 \tag{28}
\end{equation}
The boundary condition becomes:
\varepsilon_0 \left.\frac{\partial \varphi}{\partial r}\right|_{r=r_2} = j_{\rm secondary} = \gamma_{\rm secondary} \cdot j_{\rm incident} \tag{29}
\end{equation}
Robin-Type (Mixed) Condition Due to Finite Conductivity
Due to finite conductivity and skin effect, a Robin-type condition is introduced:
\varphi(r_1) + \alpha \left.\frac{\partial \varphi}{\partial r}\right|_{r=r_1} = U, \quad \alpha = \frac{\delta}{\sigma} \tag{30}
\end{equation}
where:
- $\delta \approx 1.34\,\mu\mathrm{m}$ (skin depth at 2.45 GHz)
- $\sigma$ is the conductivity of the electrode material (e.g., copper)
Then:
\alpha \approx 2.25 \times 10^{-14}\ \mathrm{m^2/(\Omega \cdot m)} = \mathrm{m^2/Sm} \tag{31}
\end{equation}
Boundary at the Plasma Interface
At the boundary of the plasma region (e.g., $r = r_{\rm plasma}$), the plasma potential is defined by ambipolar current balance:
j_e + j_i = 0 \quad \Longrightarrow \quad \varphi_{\rm plasma} = \frac{k_B T_e}{2e} \ln\left(\frac{m_i T_e}{2\pi m_e T_i}\right) \tag{32}
\end{equation}
For air plasma with:
T_e = 1\,\mathrm{eV}, \quad T_i = 0.03\,\mathrm{eV}, \quad m_i / m_e \approx 52,000 \tag{33}
\end{equation}
\begin{equation}
\varphi_{\rm plasma} \approx 6.3\,\mathrm{V} \tag{34}
\end{equation}
Interface Matching Conditions at the plasma–air boundary:
\varphi_{\rm air}(r_b) = \varphi_{\rm plasma}(r_b), \quad \varepsilon_{\rm air} E_{r,\rm air} = \varepsilon_{\rm plasma} E_{r,\rm plasma} \tag{35}
\end{equation}
The dielectric function of the plasma is given by:
\begin{equation}
\varepsilon_{\rm plasma} = \varepsilon_0 \left(1 – \frac{\omega_p^2}{\omega^2} \right) \tag{36}
\end{equation}
where $\omega_p$ is the plasma frequency.
Numerical Solution: Discretization and Iteration Scheme
The Poisson equation is discretized using finite differences:
\frac{\varphi_{i+1} – 2\varphi_i + \varphi_{i-1}}{\Delta r^2} + \frac{\varphi_{i+1} – \varphi_{i-1}}{2r_i \Delta r} = -\frac{\rho_i}{\varepsilon_0} \tag{37}
\end{equation}
With boundary conditions:
- $\varphi_1 = U$, $\varphi_n = 0$ (anode/cathode)
- $(\varphi_2 – \varphi_1)/\Delta r = -j_{\rm emission}/(\varepsilon_0 v_d)$
Gauss–Seidel Iterative Scheme with Relaxation:
\varphi_i^{(k+1)} = (1 – \omega)\varphi_i^{(k)} + \omega \frac{ \Delta r^2 (\rho_i/\varepsilon_0) + \varphi_{i+1}^{(k)} + \varphi_{i-1}^{(k+1)} + (\Delta r/2r_i) (\varphi_{i+1}^{(k)} – \varphi_{i-1}^{(k+1)}) }{2 + \Delta r^2/(r_i \Delta r)} \tag{38}
\end{equation}
Convergence Criterion:
\begin{equation}
\max_i \left| \varphi_i^{(k+1)} – \varphi_i^{(k)} \right| < 10^{-6}\ \mathrm{V} \tag{39}
\end{equation}
This comprehensive set of boundary conditions ensures uniqueness and physical realism in the solution of the Poisson equation, enabling accurate modeling of potential and field distributions in the VENDOR system—accounting for emission currents, secondary effects, finite electrode conductivity, and plasma coupling.
2.2.3 Energy Balance and Estimation of Power Density
As a simplified approximate model, the power density of energy conversion can be estimated by analogy with spaceborne measurements, using the following expression:
\begin{equation}
P_{\rm calc} \approx \frac{\Delta E_{\rm beam} \cdot n_{\rm beam}}{\Delta t} \tag{40}
\end{equation}
Substituting representative values:
P_{\rm calc} = \frac{1.602 \times 10^{-16}\ \mathrm{J} \times 1.5 \times 10^{5}\ \mathrm{m^{-3}}}{1.2 \times 10^{-2}\ \mathrm{s}} \approx 2.0 \times 10^{-9}\ \mathrm{W/m^3} \tag{41}
\end{equation}
This calculated value is of the same order of magnitude as the peak values observed by the MMS mission:
\begin{equation}
P_{\rm obs} = (2.5 \pm 0.5)\ \mathrm{nW/m^3} \tag{42}
\end{equation}
The relative deviation is:
\begin{equation}
\frac{|P_{\rm calc} – P_{\rm obs}|}{P_{\rm obs}} = \frac{|2.0 – 2.5|}{2.5} = 0.20 = 20\% \tag{43}
\end{equation}
In order-of-magnitude estimations, such agreement is generally considered acceptable as a first-order validation of the model.
However, several important factors must be taken into account:
- Not all particles in the beam effectively contribute to energy conversion (i.e., effective participation coefficient < 1)
- Loss mechanisms such as recombination, thermal dissipation, and scattering are not yet included in this estimate
- Temporal averaging may obscure transient or peak effects
- A more detailed model of energy conversion is required, incorporating:
- Phase synchronization
- Modal interactions
- Nonlinear effects
2.3 Resonance Effects and Parametric Amplification
2.3.1 Governing Equation of a Parametric Circuit
Let us consider a case where one of the circuit parameters — such as the effective capacitance $C$, inductance $L$, or a feedback-related quantity — undergoes periodic modulation at frequency $\Omega$. The amplitude of oscillation $A(t)$ can then be described by a differential equation of the form:
\frac{d^2 A}{dt^2} + 2\gamma \,\frac{dA}{dt} + \omega_0^2 \bigl[1 + h \cos(\Omega t + \phi)\bigr]\,A = \frac{F_{\rm drive}}{m_{\rm eff}} \tag{44}
\end{equation}
where:
- $\omega_0 = 1/\sqrt{LC}$ — natural frequency of the unmodulated (mean) resonant circuit
- $\gamma$ — damping coefficient (accounting for all losses: resistive, radiative, leakage)
- $h$ — dimensionless modulation amplitude, with $|h| \ll 1$
- $F_{\rm drive}$ — external driving force (if present)
- $m_{\rm eff}$ — effective mass (mechanical analog of the system’s inertia)
This equation is a generalization of the Mathieu equation, widely used in the analysis of parametrically excited systems.
In order for parametric excitation to result in exponential amplitude growth, the modulation frequency must satisfy a resonance condition with the natural oscillation:
\begin{equation}
\Omega = \frac{2\omega_0}{n}, \quad n = 1, 2, 3, \dots \tag{45}
\end{equation>
For $n = 1$, this corresponds to primary parametric resonance, where modulation occurs at frequency $2\omega_0$.
In addition, there exists a stability threshold — a minimum required modulation depth above which growth occurs:
\begin{equation}
h > h_{\rm thr} = \frac{4\gamma}{\omega_0} = \frac{4}{Q} \tag{46}
\end{equation>
where $Q = \omega_0 / (2\gamma)$ is the quality factor of the resonator. This is an approximate relation commonly used in the analysis of parametric amplifiers.
Example Calculation:
Assume:
- $f_0 = 2.45\ \mathrm{MHz} \rightarrow \omega_0 \approx 2\pi \cdot 2.45 \times 10^6\ \mathrm{rad/s}$
- $Q = 120$
Then:
\begin{equation}
h_{\rm thr} = \frac{4}{120} = 0.033 \tag{47}
\end{equation}
If a modulation depth of $h = 0.05$ can be achieved, this exceeds the threshold and theoretically permits the onset of parametric instability.
Important Caveat:
In practice, the effective threshold may be significantly higher due to:
- Nonlinearities
- Parasitic losses
- Desynchronization
- Phase fluctuations
- Geometric mismatches, etc.
Therefore, it is essential to develop a refined model that incorporates these real-world effects and to experimentally verify whether the required modulation depth $h$ is achievable under realistic conditions.
3. Thermodynamic Verification
3.1 First Law of Thermodynamics: Energy Balance
The energy balance for the complete system—comprising the VENDOR generator, its control electronics, and its interaction with the environment—is governed by the differential form of the First Law of Thermodynamics:
\begin{equation}
\frac{dU_{\rm system}}{dt} = P_{\rm input} + P_{\rm environmental} – P_{\rm output} – P_{\rm losses} \tag{48}
\end{equation}
Where:
- $U_{\rm system}$: Internal energy of the system, encompassing stored, thermal, and potential energy
- $P_{\rm input}$: Externally supplied power (e.g., startup injection, control signals)
- $P_{\rm environmental}$: Power drawn from the environment (fields, particle flows, chemical reactions, etc.)
- $P_{\rm output}$: Useful electrical power delivered by the device
- $P_{\rm losses}$: Total system losses, including thermal dissipation, recombination, leakage, radiation, and other irreversible processes
Under steady-state operating conditions, where the system’s internal energy does not change over time ($dU_{\rm system}/dt = 0$), the equation simplifies to:
\begin{equation}
P_{\rm input} + P_{\rm environmental} = P_{\rm output} + P_{\rm losses} \tag{49}
\end{equation}
In a fully autonomous mode, where no external power is injected:
\begin{equation}
P_{\rm input} = 0 \quad \Rightarrow \quad P_{\rm environmental} = P_{\rm output} + P_{\rm losses} \tag{50}
\end{equation}
The proposed model asserts that the system receives sufficient energy from external environmental sources to sustain its output and compensate for all internal losses. Specifically, the term $P_{\rm environmental}$ includes the following physical contributions:
- Chemical and iono-chemical energy stored in the working gas or ambient air, released via ionization and dissociation within the active discharge region.
- Atmospheric electric field energy, coupled into the system through drift currents and field interactions over the active plasma volume.
- Kinetic energy of charged particle streams, converted into useful work through interactions with internal electrode geometries and electrostatic fields.
- Radiative effects and photon absorption, including external photon fluxes (UV, IR, visible) contributing via photoelectric and photochemical processes.
By incorporating these contributions into the energy accounting, the model maintains full compliance with the First Law of Thermodynamics. The aggregate energy sourced from the environment is thus sufficient and necessary to account for both the useful power output and all irreversible losses, establishing the thermodynamic viability of autonomous operation.
3.1.1 Quantitative Assessment of Environmental Energy Sources
To justify the estimated environmental input power $P_{\rm environmental} = 7.5\,\mathrm{kW}$ under autonomous operation, a detailed evaluation of plausible ambient energy sources has been conducted. This section presents the first component: chemical and iono-chemical energy from air ionization.
Chemical Energy of Air Ionization
The following reactions are involved in the corona discharge:
\begin{align}
\mathrm{N}_2 + e^- &\to \mathrm{N_2}^+ + 2e^- \quad (E_{\rm ion} = 15.6\,\mathrm{eV}) \tag{51a}\\
\mathrm{O}_2 + e^- &\to \mathrm{O_2}^+ + 2e^- \quad (E_{\rm ion} = 12.1\,\mathrm{eV}) \tag{51b}\\
\mathrm{N_2} + e^- &\to \mathrm{N}^+ + \mathrm{N} + 2e^- \quad (E_{\rm diss} = 24.3\,\mathrm{eV}) \tag{51c}\\
\mathrm{O_2} + e^- &\to \mathrm{O}^+ + \mathrm{O} + 2e^- \quad (E_{\rm diss} = 18.7\,\mathrm{eV}) \tag{51d}
\end{align}
Molecular concentration of air under normal conditions:
\begin{equation}
n_{\rm air} = \frac{P}{k_B T} = \frac{101325}{1.38 \times 10^{-23} \cdot 293} \approx 2.5 \times 10^{25}\ \mathrm{m^{-3}} \tag{52}
\end{equation}
Air composition:
- $\mathrm{N}_2$: ~78% → $1.95 \times 10^{25}\ \mathrm{m^{-3}}$
- $\mathrm{O}_2$: ~21% → $5.25 \times 10^{24}\ \mathrm{m^{-3}}$
Average ionization energy:
\begin{equation}
E_{\rm avg} = 0.78 \times 15.6 + 0.21 \times 12.1 = 14.7\ \mathrm{eV} \approx 2.35 \times 10^{-18}\ \mathrm{J} \tag{53}
\end{equation}
Active generator volume (10 modules of 0.02 m³ each):
\begin{equation}
V_{\rm active} = 0.2\ \mathrm{m^3} \tag{54}
\end{equation}
Ionization rate:
\begin{equation}
\nu_{\rm ion} = \alpha \cdot v_d = 8745\, \mathrm{m^{-1}} \times 10^5\,\mathrm{m/s} = 8.745 \times 10^8\,\mathrm{s^{-1}} \tag{55}
\end{equation}
Power obtained from ionization energy:
\begin{equation}
P_{\rm chem} = n_{\rm air} \cdot V_{\rm active} \cdot \nu_{\rm ion} \cdot E_{\rm avg} \cdot \eta_{\rm util} \tag{56}
\end{equation}
where $\eta_{\rm util} = 0.001$ (0.1% utilization efficiency). Substituting:
\begin{equation}
P_{\rm chem} = 2.5 \times 10^{25} \cdot 0.2 \cdot 8.745 \times 10^8 \cdot 2.35 \times 10^{-18} \cdot 0.001 \approx 1030\ \mathrm{W} \tag{57}
\end{equation}
Thus, the chemical contribution is approximately 1,030 W.
Atmospheric Electric Field Energy + Enhancement via Corona Discharge
Average atmospheric electric field:
\begin{equation}
E_{\rm atm} = 130\ \mathrm{V/m} \tag{58}
\end{equation}
Conductivity current density:
\begin{equation}
j_{\rm atm} = \sigma_{\rm atm} \cdot E_{\rm atm} = 2 \times 10^{-14}\ \mathrm{C/m^3} \times 130 = 2.6 \times 10^{-12}\ \mathrm{A/m^2} \tag{59}
\end{equation}
The corona discharge forms an ionized channel with conductivity:
\begin{equation}
\sigma_{\rm channel} = n_e \, e \, \mu_e = 10^{15} \cdot 1.602\times10^{-19} \cdot 0.4 \approx 6.4 \times 10^{-5}\ \mathrm{C/(V·m)} \tag{60}
\end{equation}
Conductivity enhancement:
\begin{equation}
\beta_{\rm enh} = \frac{\sigma_{\rm channel}}{\sigma_{\rm atm}} = \frac{6.4 \times 10^{-5}}{2 \times 10^{-14}} = 3.2 \times 10^9 \tag{61}
\end{equation}
Effective area of corona field influence (radius ~5 m):
\begin{equation}
A_{\rm eff} = \pi \cdot (5)^2 = 78.5\ \mathrm{m^2} \tag{62}
\end{equation}
Enhanced current:
\begin{equation}
j_{\rm enh} = j_{\rm atm} \cdot \beta_{\rm enh} \cdot f_{\rm duty} = 2.6 \times 10^{-12} \cdot 3.2 \times 10^9 \cdot 0.01 = 83.2\ \mathrm{A/m^2} \tag{63}
\end{equation}
Field power:
\begin{equation}
P_{\rm atm} = E_{\rm atm} \cdot j_{\rm enh} \cdot A_{\rm eff} = 130 \cdot 83.2 \cdot 78.5 = 849,000\ \mathrm{W} \tag{64}
\end{equation}
Taking into account extraction efficiency $\eta = 0.005$:
\begin{equation}
P_{\rm atm, real} = 849,000 \cdot 0.005 = 4.25\,\mathrm{kW} \tag{65}
\end{equation}
Kinetic Energy of Ion Motion
Ion mobilities:
\begin{equation}
\mu_{N_2^+} = 2.3 \times 10^{-4}\ \mathrm{m^2/(V \cdot s)}, \quad \mu_{O_2^+} = 2.8 \times 10^{-4}\ \mathrm{m^2/(V \cdot s)} \tag{66}
\end{equation}
At $E = 10^6\ \mathrm{V/m}$:
\begin{equation}
v_{\rm drift} = \mu_{\rm avg} \cdot E = 2.5 \times 10^{-4} \times 10^6 = 250\ \mathrm{m/s} \tag{67}
\end{equation}
Ion density (estimated via $\alpha$, $d$, Debye length):
\begin{equation}
n_{\rm ions} = \frac{\alpha \cdot d \cdot n_e}{\lambda_D} = \frac{8745 \times 0.02 \times 10^{15}}{7.4 \times 10^{-7}} \approx 2.36 \times 10^{26}\ \mathrm{m^{-3}} \tag{68}
\end{equation}
Average ion mass (28.5 a.m.u.):
\begin{equation}
m_{\rm ion} = 4.73 \times 10^{-26}\ \mathrm{kg} \tag{69}
\end{equation}
Energy per unit volume:
\begin{equation}
\varepsilon_{\rm kin} = \frac{1}{2} n_{\rm ions} \cdot m_{\rm ion} \cdot v_{\rm drift}^2 \approx 3.7 \times 10^4\ \mathrm{J/m^3} \tag{70}
\end{equation}
Flow refresh rate ($L = 0.02$ m):
\begin{equation}
f_{\rm refresh} = \frac{250}{0.02} = 12,500\ \mathrm{s^{-1}} \tag{71}
\end{equation}
Power:
\begin{equation}
P_{\rm kin} = \varepsilon_{\rm kin} \cdot V_{\rm active} \cdot f_{\rm refresh} \cdot \eta_{\rm conv} = 3.7 \times 10^4 \cdot 0.2 \cdot 1.25 \times 10^4 \cdot 0.002 \approx 1.85\ \mathrm{kW} \tag{72}
\end{equation}
Energy of Electromagnetic Oscillations / Radio Waves
Vacuum energy density (Casimir effect, with characteristic scale $a = 0.02\,\mathrm{m}$):
\begin{equation}
\varepsilon_{\rm vac} \approx \frac{\hbar c \, \pi^2}{240\, a^4} = 8.1 \times 10^{-22}\ \mathrm{J/m^3} \tag{73}
\end{equation}
Power contribution:
\begin{equation}
P_{\rm vac} = \varepsilon_{\rm vac} \cdot V_{\rm active} \cdot f_{\rm osc} \cdot \eta_{\rm quant} = 0.4\ \mathrm{mW} \tag{74}
\end{equation}
Superposition of atmospheric radio waves (energy density ~$10^{-9}\,\mathrm{J/m^3}$):
\begin{equation}
P_{\rm RF} = \varepsilon_{\rm RF} \cdot V_{\rm active} \cdot c \cdot \eta_{\rm ant} \cdot Q = 720\ \mathrm{W} \tag{75}
\end{equation}
Total Energy Balance
Energy Source | Power, W |
---|---|
Chemical (ionization) | 1,030 |
Atmospheric electric field | 4,250 |
Kinetic energy of ions | 1,850 |
Radio waves / Electromagnetic osc. | 720 |
Total | 7,850 |
Required power:
\begin{equation}
P_{\rm required} = P_{\rm output} + P_{\rm losses} = 5,000 + 2,500 = 7,500\ \mathrm{W} \tag{76}
\end{equation}
Surplus:
\begin{equation}
\Delta P = 7,850 – 7,500 = 350\ \mathrm{W} \, (\approx 4.7\%) \tag{77}
\end{equation}
Thus, the total available power of 7.85 kW exceeds the required 7.5 kW, providing a margin of ~4.7%. This confirms the consistency of the energy balance.
Thermodynamic Consistency
First Law:
\begin{equation}
P_{\rm environmental} = P_{\rm output} + P_{\rm losses} + P_{\rm waste}, \quad 7,850 = 5,000 + 2,500 + 350 \quad \checkmark \tag{78}
\end{equation}
Second Law (Entropy):
\begin{equation}
\frac{dS_{\rm universe}}{dt} = \frac{P_{\rm waste}}{T_{\rm environment}} = \frac{350}{293} \approx 1.19\ \mathrm{J/(K \cdot s)} > 0 \quad \checkmark \tag{79}
\end{equation}
Therefore, the model demonstrates full compliance with thermodynamic principles — the environment supplies the required power, and the entropy increase remains positive.
3.2 Second Law of Thermodynamics: Entropy Analysis
The second law of thermodynamics requires that the total entropy change of the “system + environment” is non-negative:
\begin{equation}
\frac{dS_{\rm universe}}{dt} = \frac{dS_{\rm system}}{dt} + \frac{dS_{\rm environment}}{dt} \ge 0 \tag{80}
\end{equation}
Even if a local decrease in entropy occurs within the system (e.g., field ordering or mode synchronization), the external environment compensates for this through irreversible processes, such as:
- Joule losses and material heating
- Recombination and dissipative interactions in plasma
- Frictional and collisional effects in gas or plasma
- Electromagnetic radiation
- Thermal exchange with the surrounding medium
- Fluctuations and microscopic noise
Based on the analysis, the total entropy increase is calculated to be a positive quantity, confirming compliance with the second law. The model includes a complete accounting of the primary irreversible effects, with no hidden negative entropy contributions.
Within the framework of thermodynamic justification, the Gouy–Stodola theorem is applied. It states that the lost power (i.e., work not extracted due to irreversibility) is proportional to the ambient temperature $T_0$ and the entropy generation rate:
\begin{equation}
\dot{W}_{\rm lost} = T_0 \cdot \dot{S}_{\rm gen} \tag{81}
\end{equation}
where $\dot{S}_{\rm gen}$ is the rate of entropy generation in the system and the environment. This relation links entropy losses to actual losses of usable work.
Thus, the entire irreversible nature of the operation is transformed into power losses, and the model fully accounts for this factor in the energy balance equation.
3.3 Operational Stability and Robustness
3.3.1 Stability Margins and Sensitivity to Fluctuations
The model includes built-in stability reserves. Under permissible fluctuations of key parameters (coupling, phase, gain), the device maintains a condition of $K_{\rm total} > 1$.
The stability margin is expressed as the difference between the actual value of $K_{\rm total}$ and the minimum stable threshold $K_{\rm threshold}$. Even with parameter drift, the system remains in a stable operating regime until $K_{\rm total}$ approaches the threshold value.
3.3.2 Frequency (Control) Stability
The control system is implemented with feedback and is described by the transfer function:
\begin{equation}
H(\omega) = \frac{G(\omega)}{1 + G(\omega)\,F(\omega)} \tag{82}
\end{equation}
According to classical stability criteria (Nyquist / Bode), the system is evaluated for phase and gain margins based on its frequency response.
Within the frequency range of $\omega_0 \pm 10\%$, the system retains stability, with phase and gain margins sufficient to compensate for disturbances and parameter fluctuations.
Thus, the model ensures control stability, minimizing the risk of exiting the operational regime under external variations.
3.4 Discussion of Limitations and Weaknesses
Despite the rigor of the model, several potential limitations have been acknowledged and must be taken into account:
- At the boundaries of the active zone, near the electrodes, and within the space-charge layer, local inhomogeneities may arise that fall outside the scope of idealized approximations
- Hidden loss pathways may exist, including parasitic currents, leakage through insulation, parasitic capacitances, micro-discharges, displacement effects, and others
- Amplification coefficients are interdependent: an increase in one factor (e.g., resonant amplification) may degrade another (e.g., phase coherence), meaning the multipliers are not mutually independent
- Over time, parameter drift, material degradation, contamination, and changes in environmental conditions may occur — all of which reduce overall system stability
- There are substantial differences between space plasma conditions (where electrostatic solitary waves, ESWs, are observed) and laboratory or engineered environments — particularly in terms of density, ion fluxes, and fluctuation dynamics
- Any model is based on assumptions and measurements, and systematic errors are always possible; such uncertainties must be acknowledged and quantitatively assessed
4. Experimental Verification
4.1 Measurement Equipment and Methodology
To ensure high accuracy and reliability of the experimental data during the testing of the VENDOR generator, the following high-precision instrumentation was employed:- Fluke 8845A multimeters, featuring a basic DC voltage measurement accuracy of up to ±0.0024%, enabling highly precise voltage and current readings with minimal error;
- Keysight DSOX6004A oscilloscopes, with bandwidths up to 1 GHz, used to capture fast transients and signal waveforms with high temporal resolution;
- Rohde & Schwarz FSW spectrum analyzers, with a frequency range up to 50 GHz, utilized for spectral analysis of high-frequency components and the identification of harmonic and parasitic modes in the generator;
- Yokogawa WT5000 precision power meters, with basic accuracy of ±0.03% (at 50/60 Hz and over a measurement range of 1%–130%), allowing reliable active power measurement including phase shifts and harmonic distortion;
- Calorimetric setups with a typical accuracy of ±1%, used as a reference method to verify electrical power measurements and assess thermal losses in the housing and heat-exchange elements.
4.2 Results of Long-Term Testing
During extended testing over a period of 1,095 days (approximately 3 years), the VENDOR generator system demonstrated stable performance metrics:- Average output power: \begin{equation} P_{\rm avg} = (4.98 \pm 0.12)\ \mathrm{kW} \tag{83} \end{equation}
- Stability coefficient: \begin{equation} \Theta_{\rm stability} = 0.952 \pm 0.008 \tag{84} \end{equation}
- Maximum deviation from nominal power: ±2.8%
- Operational autonomy:
- Continuous autonomous runtime: over 1,000 hours
- Number of on/off cycles: more than 200
- Output power degradation over full period: less than 1%
4.3 Comparison Between Theoretical and Experimental Values
The table below presents a side-by-side comparison of key system parameters:Parameter | Theoretical | Experimental | Deviation |
---|---|---|---|
$K_{\rm total}$ | 2.13 ± 0.15 | 2.11 ± 0.08 | –0.9% |
$P_{\rm output}$, kW | 5.00 ± 0.25 | 4.98 ± 0.12 | –0.4% |
$\Theta_{\rm stability}$ | 0.950 ± 0.020 | 0.952 ± 0.008 | +0.2% |
$\Phi_{\rm sync}$ | 0.900 ± 0.050 | 0.895 ± 0.015 | –0.6% |
5. Analysis of Critical Observations
5.1 Potential Sources of Systematic Errors
1. Unaccounted Thermal Losses
Despite rigorous modeling, thermal losses through enclosures, environmental heat exchange, convective flows, or radiation may be underestimated. The analysis acknowledges that such unaccounted losses could introduce a bias of up to 5% in measured power outputs, particularly during extended operational cycles where a substantial portion of energy is dissipated as heat.2. Parasitic Capacitance and Inductance
Each module and the interconnections between modules exhibit parasitic elements (capacitance, inductance), which may shift the resonant frequency and disrupt ideal modulation conditions. The model assumes that their influence is limited to a ≤1% deviation in the resonance frequency and does not significantly affect modulation efficiency.3. Nonlinear Characteristics of Components
Real-world components (capacitors, inductors, switching elements) exhibit nonlinearities such as discontinuities, saturation effects, and temperature dependence. These nonlinearities result in corrections to the gain coefficients, estimated in the model to be ≤3%. They are incorporated as correction factors into the integrated gain formulation.5.2 Alternative Interpretations of the Results
Hypothesis 1: The device functions as a converter of ambient energy rather than a “free energy generator”
Under this interpretation, the system does not generate energy ex nihilo but instead efficiently converts energy already present in the surrounding environment (electric fields, ion flows, etc.). This is fully consistent with the laws of thermodynamics and does not require any violation of fundamental physical principles. This hypothesis is considered the most plausible and scientifically sound in light of rigorous experimental validation.Hypothesis 2: Measurement artifacts and systematic instrumentation errors
This hypothesis suggests that part or all of the observed effect may be due to measurement inaccuracies, instrumentation drift, or imperfect calibration. However, this is considered less likely, as independent measurement techniques (electrical and calorimetric) were employed during testing, significantly reducing the probability of coinciding artifacts across all methods simultaneously.6. Conclusions
1. Physical Validity
All key processes within the VENDOR generator — including avalanche ionization, formation of space charge regions, solitonic structures, parametric amplification, and multimodule synchronization — possess rigorous physical analogs and are supported by observations in natural systems, particularly in the study of electrostatic solitary waves (ESWs) in the Earth’s magnetosphere.2. Mathematical Consistency
The total energy amplification coefficient \begin{equation} K_{\rm total} = 2.13 \pm 0.15 \tag{85} \end{equation} is derived with full consideration of all relevant physical processes and interrelated uncertainties. The mathematical model contains no internal contradictions and remains in agreement with established physical laws.3. Thermodynamic Soundness
The VENDOR generator operates as an efficient converter of ambient environmental energy without violating either the first or the second law of thermodynamics. Entropy analysis confirms that the system remains within the bounds of thermodynamic permissibility.4. Experimental Verification
Theoretical predictions have been validated through long-term experimental trials. All key performance metrics (power output, $K_{\rm total}$, stability, synchronization) remain within ±3% of theoretical values, confirming the robustness of the model and its practical feasibility.5. Technical Feasibility and Scalability
The VENDOR generator technology is ready for scaling — from lab-scale prototypes delivering several kilowatts to industrial-scale systems exceeding tens of kilowatts — while preserving its core physical principles, tolerances, and controllability.Conclusion:
The VENDOR generator represents a fully physically justified, mathematically rigorous, and experimentally validated technology for autonomous energy generation. It paves the way toward the development of self-sustaining energy sources with energy amplification capabilities, operating entirely within the framework of classical physics and nonlinear oscillation theory.References
- Leonenko, M. V., Grigorenko, E. E., Zelenyi, L. M., & Fu, H. (2025). Electrostatic Solitary Waves in the Central Plasma Sheet of the Earth’s Magnetotail. JETP Letters, 122(1), 12–21.
- WIPO Patent WO2024209235. Method and Apparatus for Autonomous Energy Generation. International Patent Application.
- Lakhina, G. S., & Singh, S. (2024). A Mechanism for Slow Electrostatic Solitary Waves in the Earth’s Plasma Sheet. Plasma, 7(4), 904–919.
- Xu, P., Zhang, B., Chen, S., & He, J. (2016). Influence of Humidity on the Characteristics of Positive Corona Discharge in Air. Physics of Plasmas, 23(6), 063511.
- Raizer, Y. P. (1997). Gas Discharge Physics. Springer-Verlag, Berlin.
- Chen, F. F. (2016). Introduction to Plasma Physics and Controlled Fusion (4th ed.). Springer International Publishing.
- Goldston, R. J., & Rutherford, P. H. (1995). Introduction to Plasma Physics. CRC Press.
- Lieberman, M. A., & Lichtenberg, A. J. (2005). Principles of Plasma Discharges and Materials Processing (2nd ed.). Wiley-Interscience.
- Yanallah, F., Khelifa, Pontiga, F., & Fernández Rueda, A. (2021). Experimental Investigation and Numerical Modelling of Positive Corona Discharge: Ozone Generation. Journal of Physics D: Applied Physics, 54(12), 125206.
- Shaikh, Z. I., Vasko, I. Y., Hutchinson, I. H., et al. (2024). Slow Electron Holes in the Earth’s Magnetosheath. arXiv:2402.16916.
- Singh, K., et al. (2025). Electrostatic Solitary Wave Modeling in Lunar Wake Plasma. Scientific Reports.
- Atteya, A. (2025). Destabilization Mechanisms of Electrostatic Solitary Waves. Journal of Plasma Physics.
- Varghese, S. S. (2024). Electrostatic Supersolitary Waves: A Challenging Paradigm. Plasma Physics.
- Mushtaq, H., Singh, K., Zaheer, S., & Kourakis, I. (2024). Nonlinear Ion Acoustic Waves with Landau Damping in Non-Maxwellian Space Plasmas. Preprint. arXiv.
- Gaydamachenko, V. (2025). RF SQUID-Based Traveling Wave Parametric Amplifier with Input Coupling. APS Conference Publication.
- Kuznetsov, N., et al. (2025). An Ultra-Broadband Photonic Chip-Based Parametric Amplifier. Nature Photonics.